Advanced analyte sensor calibration and error detection

ABSTRACT

Systems and methods for processing sensor data and self-calibration are provided. In some embodiments, systems and methods are provided which are capable of calibrating a continuous analyte sensor based on an initial sensitivity, and then continuously performing self-calibration without using, or with reduced use of, reference measurements. In certain embodiments, a sensitivity of the analyte sensor is determined by applying an estimative algorithm that is a function of certain parameters. Also described herein are systems and methods for determining a property of an analyte sensor using a stimulus signal. The sensor property can be used to compensate sensor data for sensitivity drift, or determine another property associated with the sensor, such as temperature, sensor membrane damage, moisture ingress in sensor electronics, and scaling factors.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/476,145 filed Apr. 15, 2011. The aforementioned application isincorporated by reference herein in its entirety, and is herebyexpressly made a part of this specification.

TECHNICAL FIELD

The embodiments described herein relate generally to systems and methodsfor processing sensor data from continuous analyte sensors and forself-calibration.

BACKGROUND

Diabetes mellitus is a chronic disease, which occurs when the pancreasdoes not produce enough insulin (Type I), or when the body cannoteffectively use the insulin it produces (Type II). This conditiontypically leads to an increased concentration of glucose in the blood(hyperglycemia), which can cause an array of physiological derangements(e.g., kidney failure, skin ulcers, or bleeding into the vitreous of theeye) associated with the deterioration of small blood vessels.Sometimes, a hypoglycemic reaction (low blood sugar) is induced by aninadvertent overdose of insulin, or after a normal dose of insulin orglucose-lowering agent accompanied by extraordinary exercise orinsufficient food intake.

A variety of sensor devices have been developed for continuouslymeasuring blood glucose concentrations. Conventionally, a diabeticperson carries a self-monitoring blood glucose (SMBG) monitor, whichtypically involves uncomfortable finger pricking methods. Due to a lackof comfort and convenience, a diabetic will often only measure his orher glucose levels two to four times per day. Unfortunately, thesemeasurements can be spread far apart, such that a diabetic may sometimeslearn too late of a hypoglycemic or hyperglycemic event, therebypotentially incurring dangerous side effects. In fact, not only is itunlikely that a diabetic will take a timely SMBG measurement, but evenif the diabetic is able to obtain a timely SMBG value, the diabetic maynot know whether his or her blood glucose value is increasing ordecreasing, based on the SMBG alone.

Heretofore, a variety of glucose sensors have been developed forcontinuously measuring glucose values. Many implantable glucose sensorssuffer from complications within the body and provide only short-termand less-than-accurate sensing of blood glucose. Similarly, transdermalsensors have run into problems in accurately sensing and reporting backglucose values continuously over extended periods of time. Some effortshave been made to obtain blood glucose data from implantable devices andretrospectively determine blood glucose trends for analysis; howeverthese efforts do not aid the diabetic in determining real-time bloodglucose information. Some efforts have also been made to obtain bloodglucose data from transdermal devices for prospective data analysis,however similar problems have occurred.

SUMMARY OF THE INVENTION

In a first aspect, a method is provided for calibrating sensor datagenerated by a continuous analyte sensor, comprising: generating sensordata using a continuous analyte sensor; iteratively determining, with anelectronic device, a sensitivity value of the continuous analyte sensoras a function of time by applying a priori information comprising sensorsensitivity information; and calibrating the sensor data based at leastin part on the determined sensitivity value.

In an embodiment of the first aspect or any other embodiment thereof,calibrating the sensor data is performed iteratively throughout asubstantially entire sensor session.

In an embodiment of the first aspect or any other embodiment thereof,iteratively determining a sensitivity value is performed at regularintervals or performed at irregular intervals, as determined by the apriori information.

In an embodiment of the first aspect or any other embodiment thereof,iteratively determining a sensitivity value is performed throughout asubstantially entire sensor session.

In an embodiment of the first aspect or any other embodiment thereof,determining a sensitivity value is performed in substantially real time.

In an embodiment of the first aspect or any other embodiment thereof,the a priori information is associated with at least one predeterminedsensitivity value that is associated with a predetermined time afterstart of a sensor session.

In an embodiment of the first aspect or any other embodiment thereof, atleast one predetermined sensitivity value is associated with acorrelation between a sensitivity determined from in vitro analyteconcentration measurements and a sensitivity determined from in vivoanalyte concentration measurements at the predetermined time.

In an embodiment of the first aspect or any other embodiment thereof,the a priori information is associated with a predetermined sensitivityfunction that uses time as input.

In an embodiment of the first aspect or any other embodiment thereof,time corresponds to time after start of a sensor session.

In an embodiment of the first aspect or any other embodiment thereof,time corresponds to at least one of time of manufacture or time sincemanufacture.

In an embodiment of the first aspect or any other embodiment thereof,the sensitivity value of the continuous analyte sensor is also afunction of at least one other parameter.

In an embodiment of the first aspect or any other embodiment thereof,the at least one other parameter is selected from the group consistingof: temperature, pH, level or duration of hydration, curing condition,an analyte concentration of a fluid surrounding the continuous analytesensor during startup of the sensor, and combinations thereof.

In an embodiment of the first aspect or any other embodiment thereof.calibrating the sensor data is performed without using reference bloodglucose data.

In an embodiment of the first aspect or any other embodiment thereof,the electronic device is configured to provide a level of accuracycorresponding to a mean absolute relative difference of no more thanabout 10% over a sensor session of at least about 3 days, and whereinreference measurements associated with calculation of the mean absoluterelative difference are determined by analysis of blood.

In an embodiment of the first aspect or any other embodiment thereof,the sensor session is at least about 4 days.

In an embodiment of the first aspect or any other embodiment thereof,the sensor session is at least about 5 days.

In an embodiment of the first aspect or any other embodiment thereof,the sensor session is at least about 6 days.

In an embodiment of the first aspect or any other embodiment thereof,the sensor session is at least about 7 days.

In an embodiment of the first aspect or any other embodiment thereof,the sensor session is at least about 10 days.

In an embodiment of the first aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 7% over thesensor session.

In an embodiment of the first aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 5% over thesensor session.

In an embodiment of the first aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 3% over thesensor session.

In an embodiment of the first aspect or any other embodiment thereof,the a priori information is associated with a calibration code.

In an embodiment of the first aspect or any other embodiment thereof,the a priori sensitivity information is stored in the sensor electronicsprior to use of the sensor.

In a second aspect, a system is provided for implementing the method ofthe first aspect or any embodiments thereof.

In a third aspect, a method is provided for calibrating sensor datagenerated by a continuous analyte sensor, the method comprising:generating sensor data using a continuous analyte sensor; determining,with an electronic device, a plurality of different sensitivity valuesof the continuous analyte sensor as a function of time and ofsensitivity information associated with a priori information; andcalibrating the sensor data based at least in part on at least one ofthe plurality of different sensitivity values.

In an embodiment of the third aspect or any other embodiment thereof,calibrating the continuous analyte sensor is performed iterativelythroughout a substantially entire sensor session.

In an embodiment of the third aspect or any other embodiment thereof,the plurality of different sensitivity values are stored in a lookuptable in computer memory.

In an embodiment of the third aspect or any other embodiment thereof,determining a plurality of different sensitivity values is performedonce throughout a substantially entire sensor session.

In an embodiment of the third aspect or any other embodiment thereof,the a priori information is associated with at least one predeterminedsensitivity value that is associated with a predetermined time afterstart of a sensor session.

In an embodiment of the third aspect or any other embodiment thereof,the at least one predetermined sensitivity value is associated with acorrelation between a sensitivity determined from in vitro analyteconcentration measurements and a sensitivity determined from in vivoanalyte concentration measurements at the predetermined time.

In an embodiment of the third aspect or any other embodiment thereof,the a priori information is associated with a predetermined sensitivityfunction that uses time as input.

In an embodiment of the third aspect or any other embodiment thereof,time corresponds to time after start of a sensor session.

In an embodiment of the third aspect or any other embodiment thereof,time corresponds to time of manufacture or time since manufacture.

In an embodiment of the third aspect or any other embodiment thereof,the plurality of sensitivity values are also a function of at least oneparameter other than time.

In an embodiment of the third aspect or any other embodiment thereof,the at least one other parameter is selected from the group consistingof: temperature, pH, level or duration of hydration, curing condition,an analyte concentration of a fluid surrounding the continuous analytesensor during startup of the sensor, and combinations thereof.

In an embodiment of the third aspect or any other embodiment thereof,calibrating the continuous analyte sensor is performed without usingreference blood glucose data.

In an embodiment of the third aspect or any other embodiment thereof,the electronic device is configured to provide a level of accuracycorresponding to a mean absolute relative difference of no more thanabout 10% over a sensor session of at least about 3 days; and whereinreference measurements associated with calculation of the mean absoluterelative difference are determined by analysis of blood.

In an embodiment of the third aspect or any other embodiment thereof,the sensor session is at least about 4 days.

In an embodiment of the third aspect or any other embodiment thereof,the sensor session is at least about 5 days.

In an embodiment of the third aspect or any other embodiment thereof,the sensor session is at least about 6 days.

In an embodiment of the third aspect or any other embodiment thereof,the sensor session is at least about 7 days.

In an embodiment of the third aspect or any other embodiment thereof,the sensor session is at least about 10 days.

In an embodiment of the third aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 7% over thesensor session.

In an embodiment of the third aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 5% over thesensor session.

In an embodiment of the third aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 3% over thesensor session.

In an embodiment of the third aspect or any other embodiment thereof,the a priori information is associated with a calibration code.

In a fourth aspect, a system is provided for implementing the method ofthe third aspect or any embodiments thereof.

In a fifth aspect, a method is provided for processing data from acontinuous analyte sensor, the method comprising: receiving, with anelectronic device, sensor data from a continuous analyte sensor, thesensor data comprising at least one sensor data point; iterativelydetermining a sensitivity value of the continuous analyte sensor as afunction of time and of an at least one predetermined sensitivity valueassociated with a predetermined time after start of a sensor session;forming a conversion function based at least in part on the sensitivityvalue; and determining an analyte output value by applying theconversion function to the at least one sensor data point.

In an embodiment of the fifth aspect or any other embodiment thereof,the iteratively determining a sensitivity of the continuous analytesensor is performed continuously.

In an embodiment of the fifth aspect or any other embodiment thereof,iteratively determining a sensitivity is performed in substantially realtime.

In an embodiment of the fifth aspect or any other embodiment thereof,the method further comprises determining a baseline of the continuousanalyte sensor, and wherein the conversion function is based at least inpart on the baseline.

In an embodiment of the fifth aspect or any other embodiment thereof,determining a baseline of the continuous analyte sensor is performedcontinuously.

In an embodiment of the fifth aspect or any other embodiment thereof,determining a sensitivity of the continuous analyte sensor anddetermining a baseline of the analyte sensor are performed atsubstantially the same time.

In an embodiment of the fifth aspect or any other embodiment thereof,the at least one predetermined sensitivity value is set at amanufacturing facility for the continuous analyte sensor.

In an embodiment of the fifth aspect or any other embodiment thereof,the method further comprises receiving at least one calibration code;and applying the at least one calibration code to the electronic deviceat a predetermined time after start of the sensor session.

In an embodiment of the fifth aspect or any other embodiment thereof,iteratively determining a sensitivity is performed at regular intervalsor performed at irregular intervals, as determined by the at least onecalibration code.

In an embodiment of the fifth aspect or any other embodiment thereof,the at least one calibration code is associated with the at least onepredetermined sensitivity.

In an embodiment of the fifth aspect or any other embodiment thereof,the at least one calibration code is associated with a predeterminedsensitivity function that uses time of the function of time as input.

In an embodiment of the fifth aspect or any other embodiment thereof,time corresponds to time after start of the sensor session.

In an embodiment of the fifth aspect or any other embodiment thereof,time corresponds to time of manufacture or time since manufacture.

In an embodiment of the fifth aspect or any other embodiment thereof,the sensitivity value of the continuous analyte sensor is also afunction of at least one other parameter.

In an embodiment of the fifth aspect or any other embodiment thereof,the at least one other parameter is selected from the group consistingof: temperature, pH, level or duration of hydration, curing condition,an analyte concentration of a fluid surrounding the continuous analytesensor during startup of the sensor, and combinations thereof.

In a sixth aspect, a system is provided for implementing the method ofthe fifth aspect or any embodiments thereof.

In a seventh aspect, a method is provided for calibrating a continuousanalyte sensor, the method comprising: receiving sensor data from acontinuous analyte sensor; forming or receiving a predeterminedsensitivity profile corresponding to a change in sensor sensitivity toan analyte over a substantially entire sensor session, wherein thepredetermined sensitivity profile is a function of at least onepredetermined sensitivity value associated with a predetermined timeafter start of the sensor session; and applying, with an electronicdevice, the sensitivity profile in real-time calibrations.

In an embodiment of the seventh aspect or any other embodiment thereof,the at least one predetermined sensitivity value, the predeterminedsensitivity profile, or both are set at a manufacturing facility for thecontinuous analyte sensor.

In an embodiment of the seventh aspect or any other embodiment thereof,the method further comprises receiving at least one calibration code;and applying the at least one calibration code to the electronic deviceat a predetermined time after start of the sensor session.

In an embodiment of the seventh aspect or any other embodiment thereof,the at least one calibration code is associated with the at least onepredetermined sensitivity.

In an embodiment of the seventh aspect or any other embodiment thereof,the at least one calibration code is associated with a predeterminedsensitivity function that uses time as input.

In an embodiment of the seventh aspect or any other embodiment thereof,the sensitivity profile is a function of time.

In an embodiment of the seventh aspect or any other embodiment thereof,time corresponds to time after start of the sensor session.

In an embodiment of the seventh aspect or any other embodiment thereof,time corresponds to time of manufacture or time since manufacture.

In an embodiment of the seventh aspect or any other embodiment thereof,the sensitivity value is a function of time, the predeterminedsensitivity value, and at least one parameter selected from the groupconsisting of: temperature, pH, level or duration of hydration, curingcondition, an analyte concentration of a fluid surrounding thecontinuous analyte sensor during startup of the sensor, and combinationsthereof.

In an eighth aspect, a system is provided for implementing the method ofthe seventh aspect or any embodiments thereof.

In a ninth aspect, a method is provided for processing data from acontinuous analyte sensor, the method comprising: receiving, with anelectronic device, sensor data from a continuous analyte sensor, thesensor data comprising at least one sensor data point; receiving orforming a sensitivity profile corresponding to a change in sensorsensitivity over a substantially entire sensor session; forming aconversion function based at least in part on the sensitivity profile;and determining an analyte output value by applying the conversionfunction to the at least one sensor data point.

In an embodiment of the ninth aspect or any other embodiment thereof,the sensitivity profile is set at a manufacturing facility for thecontinuous analyte sensor.

In an embodiment of the ninth aspect or any other embodiment thereof,the method comprises receiving at least one calibration code; andapplying the at least one calibration code to the electronic device at apredetermined time after start of the sensor session.

In an embodiment of the ninth aspect or any other embodiment thereof,the at least one calibration code is associated with the at least onepredetermined sensitivity.

In an embodiment of the ninth aspect or any other embodiment thereof,the at least one calibration code is associated with the sensitivityprofile.

In an embodiment of the ninth aspect or any other embodiment thereof,the sensitivity profile is a function of time.

In an embodiment of the ninth aspect or any other embodiment thereof,time corresponds to time after start of the sensor session.

In an embodiment of the ninth aspect or any other embodiment thereof,time corresponds to time of manufacture or time since manufacture.

In an embodiment of the ninth aspect or any other embodiment thereof,the sensitivity is a function of time and at least one parameter isselected from the group consisting of: temperature, pH, level orduration of hydration, curing condition, an analyte concentration of afluid surrounding the continuous analyte sensor during startup of thesensor, and combinations thereof.

In a tenth aspect, a system is provided for implementing the method ofthe ninth aspect or any embodiments thereof.

In an eleventh aspect, a system is provided for monitoring analyteconcentration in a host, the system comprising: a continuous analytesensor configured to measure analyte concentration in a host and toprovide factory-calibrated sensor data, the factory-calibrated sensordata being calibrated without reference blood glucose data; wherein thesystem is configured to provide a level of accuracy corresponding to amean absolute relative difference of no more than about 10% over asensor session of at least about 3 days, wherein reference measurementsassociated with calculation of the mean absolute relative difference aredetermined by analysis of blood.

In an embodiment of the eleventh aspect or any other embodiment thereof,the sensor session is at least about 4 days.

In an embodiment of the eleventh aspect or any other embodiment thereof,the sensor session is at least about 5 days.

In an embodiment of the eleventh aspect or any other embodiment thereof,the sensor session is at least about 6 days.

In an embodiment of the eleventh aspect or any other embodiment thereof,the sensor session is at least about 7 days.

In an embodiment of the eleventh aspect or any other embodiment thereof,the sensor session is at least about 10 days.

In an embodiment of the eleventh aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 7% over thesensor session.

In an embodiment of the eleventh aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 5% over thesensor session.

In an embodiment of the eleventh aspect or any other embodiment thereof,the mean absolute relative difference is no more than about 3% over thesensor session.

In a twelfth aspect, a method for determining a property of a continuousanalyte sensor, the method comprising: applying a bias voltage to ananalyte sensor; applying a voltage step above the bias voltage to theanalyte sensor; measuring, using sensor electronics, a signal responseof the voltage step; determining, using the sensor electronics, a peakcurrent of the signal response; determining, using the sensorelectronics, a property of the sensor by correlating the peak current toa predetermined relationship.

In an embodiment of the twelfth aspect or any other embodiment thereof,correlating the peak current to the predetermined relationship comprisescalculating an impedance of the sensor based on the peak current andcorrelating the sensor impedance to the predetermined relationship.

In an embodiment of the twelfth aspect or any other embodiment thereof,the property of the sensor is a sensitivity of the sensor or atemperature of the sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof,the peak current is a difference between a magnitude of the responseprior to the voltage step and a magnitude of the largest measuredresponse resulting from the voltage step.

In an embodiment of the twelfth aspect or any other embodiment thereof,the predetermined relationship is an impedance-to-sensor sensitivityrelationship, and wherein the property of the sensor is a sensitivity ofthe sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof,the method further comprises compensating sensor data using thedetermined property of the sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof,the compensating comprises correlating a predetermined relationship ofthe peak current to sensor sensitivity or change in sensor sensitivityand modifying a value or values of the sensor data responsive to thecorrelated sensor sensitivity or change in sensor sensitivity.

In an embodiment of the twelfth aspect or any other embodiment thereof,predetermined relationship is a linear relationship over time of use ofthe analyte sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof,the predetermined relationship is a non-linear relationship over time ofuse of the analyte sensor.

In an embodiment of the twelfth aspect or any other embodiment thereof,wherein the predetermined relationship is determined by prior testing ofsensors similar to the analyte sensor.

In an embodiment of the thirteenth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or more processorof the sensor system, cause the sensor system to implement the method ofthe twelfth aspect or any embodiment thereof.

In a fourteenth aspect, a method is provided for calibrating an analytesensor, the method comprising: applying a time-varying signal to theanalyte sensor; measuring a signal response to the applied signal;determining, using sensor electronics, a sensitivity of the analytesensor, the determining comprising correlating at least one property ofthe signal response to a predetermined sensor sensitivity profile; andgenerating, using sensor electronics, estimated analyte concentrationvalues using the determined sensitivity and sensor data generated by theanalyte sensor.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the sensitivity profile comprises varying sensitivity valuesover time since implantation of the sensor.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the predetermined sensitivity profile comprises a plurality ofsensitivity values.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the predetermined sensitivity profile is based on sensorsensitivity data generated from studying sensitivity changes of analytesensors similar to the analyte sensor.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the method further comprises applying a bias voltage to thesensor, wherein the time-varying signal comprises a step voltage abovethe bias voltage or a sine wave overlaying a voltage bias voltage.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the determining further comprises calculating an impedancevalue based on the measured signal response and correlating theimpedance value to a sensitivity value of the predetermined sensitivityprofile.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the method further comprises applying a DC bias voltage to thesensor to generate sensor data, wherein the estimating analyteconcentration values includes generating corrected sensor data using thedetermined sensitivity.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the method further comprises applying a conversion function tothe corrected sensor data to generate the estimated analyteconcentration values.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the method further comprises forming a conversion functionbased at least in part of the determined sensitivity, and wherein theconversion function is applied to the sensor data to generate theestimated analyte concentration values.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the property is a peak current value of the signal response.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the determining further comprises using at least one ofperforming a Fast Fourier Transform on the signal response data,integrating at least a portion of a curve of the signal response, anddetermining a peak current of the signal response.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the determining further comprises selecting the predeterminedsensitivity profile based on the determined sensor property from aplurality of different predetermined sensitivity profiles.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the selecting includes performing a data association analysisto determine a correlation between the determined sensor property andeach of the plurality of different predetermined sensitivity profilesand wherein the selected predetermined sensitivity profile has thehighest correlation.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the method further comprises generating estimated analyteconcentration values using the selected sensitivity profile.

In an embodiment of the fourteenth aspect or any other embodimentthereof, the method further comprises determining a second sensitivityvalue using the selected sensitivity profile, wherein a first set ofestimated analyte concentration values is generated using the determinedsensitivity value and sensor data associated with a first time period,and wherein a second set of concentration values is generated using thesecond sensitivity value and sensor data associated with a second timeperiod.

In a fifteenth aspect, a sensor system is provided for implementing themethod of the fourteenth aspect or any embodiments thereof.

In an embodiment of the fifteenth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the fourteenth aspect or any embodiment thereof.

In a sixteenth aspect, a method is provided for determining whether ananalyte sensor system is functioning properly, the method comprising:applying a stimulus signal to the analyte sensor; measuring a responseto the stimulus signal; estimating a value of a sensor property based onthe signal response; correlating the sensor property value with apredetermined relationship of the sensor property and a predeterminedsensor sensitivity profile; and initiating an error routine if thecorrelation does not exceed a predetermined correlation threshold.

In an embodiment of the sixteenth aspect or any other embodimentthereof, correlating includes performing a data association analysis.

In an embodiment of the sixteenth aspect or any other embodimentthereof, the error routine comprises displaying a message to a userindicating that the analyte sensor is not functioning properly.

In an embodiment of the sixteenth aspect or any other embodimentthereof, the sensor property is an impedance of the sensor membrane.

In a seventeenth aspect, a sensor system is provided configured toimplement the method of the sixteenth aspect or any embodiment thereof.

In an embodiment of the seventeenth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the sixteenth aspect or any embodiment thereof.

In an eighteenth aspect, a method is provided for determining atemperature associated with an analyte sensor, the method comprising:applying a stimulus signal to the analyte sensor; measuring a signalresponse of the signal; and determining a temperature associated with ofthe analyte sensor, the determining comprising correlating at least oneproperty of the signal response to a predetermined relationship of thesensor property to temperature.

In an embodiment of the eighteenth aspect or any other embodimentthereof, the method further comprises generating estimated analyteconcentration values using the determined temperature and sensor datagenerated from the analyst sensor.

In an embodiment of the eighteenth aspect or any other embodimentthereof, the generating includes compensating the sensor data using thedetermined temperature and converting the compensated sensor data to thegenerated estimated analyte values using a conversion function.

In an embodiment of the eighteenth aspect or any other embodimentthereof, the generating includes forming or modifying a conversionfunction using the determined temperature and converting the sensor datato the generated estimated analyte values using the formed or modifiedconversion function.

In an embodiment of the eighteenth aspect or any other embodimentthereof, the method further comprises measuring a temperature using asecond sensor, wherein the determining further comprises using themeasured temperature to determine the temperature associated with theanalyte sensor.

In an embodiment of the eighteenth aspect or any other embodimentthereof, the second sensor is a thermistor.

In a nineteenth aspect, a sensor system is provided configured toimplement the methods of the eighteenth aspect or any embodimentthereof.

In an embodiment of the nineteenth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the eighteenth aspect or any embodiment thereof.

In a twentieth aspect, a method is provided for determining moistureingress in an electronic sensor system, comprising: applying a stimulussignal having a particular frequency or a signal comprising a spectrumof frequencies to an analyte sensor; measuring a response to thestimulus signal; calculating, using sensor electronics, an impedancebased on the measured signal response; determining, using sensorelectronics, whether the impedance falls within a predefined levelcorresponding to moisture ingress; initiating, using sensor electronics,an error routine if the impedance exceeds one or both of the respectivepredefined levels

In an embodiment of the twentieth aspect or any other embodimentthereof, the method further comprises the error routine includes one ormore of triggering an audible alarm and a visual alarm on a displayscreen to alert a user that the sensor system may not be functioningproperly.

In an embodiment of the twentieth aspect or any other embodimentthereof, the stimulus signal has a predetermined frequency.

In an embodiment of the twentieth aspect or any other embodimentthereof, the stimulus signal comprises a spectrum of frequencies.

In an embodiment of the twentieth aspect or any other embodimentthereof, the calculated impedance comprises a magnitude value and aphase value, and wherein the determination comprises comparing theimpedance magnitude value to a predefined impedance magnitude thresholdand the phase value to a predefined phase threshold.

In an embodiment of the twentieth aspect or any other embodimentthereof, the calculated impedance is a complex impedance value.

In a twenty-first aspect, a sensor system is provided configured toimplement the methods of the twentieth aspect or any embodiment thereof.

In an embodiment of the twenty-first aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the twentieth aspect or any embodiment thereof.

In an twenty-second aspect, a method is provided for determiningmembrane damage of an analyte sensor using a sensor system, comprising:applying a stimulus signal to an analyte sensor; measuring a response tothe stimulus signal; calculating, using sensor electronics, an impedancebased on the signal response; determining, using the sensor electronics,whether the impedance falls within a predefined level corresponding tomembrane damage; and initiating, using the sensor electronics, an errorroutine if the impedance exceeds the predefined level.

In an embodiment of the twenty-second aspect or any other embodimentthereof, the error routine includes triggering one or more of an audiblealarm and a visual alarm on a display screen.

In an embodiment of the twenty-second aspect or any other embodimentthereof, the stimulus signal has a predetermined frequency.

In an embodiment of the twenty-second aspect or any other embodimentthereof, the stimulus signal comprises a spectrum of frequencies.

In an embodiment of the twenty-second aspect or any other embodimentthereof, the calculated impedance comprises a magnitude value and aphase value, and wherein the determination comprises comparing theimpedance magnitude value to a predefined impedance magnitude thresholdand the phase value to a predefined phase threshold.

In an embodiment of the twenty-second aspect or any other embodimentthereof, the calculated impedance is a complex impedance value.

In a twenty-third aspect, a sensor system is provided configured toimplement the methods of the twenty-second aspect or any embodimentthereof.

In an embodiment of the twenty-third aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the twenty-second aspect or any embodiment thereof.

In a twenty-fourth aspect, a method for determining reuse of an analytesensor, comprising, applying a stimulus signal to an analyte sensor;measuring a response of the stimulus signal; calculating an impedanceresponse based on the response; comparing the calculated impedance to apredetermined threshold; initiating a sensor reuse routine if it isdetermined that the impedance exceeds the threshold.

In an embodiment of the twenty-fourth aspect or any other embodimentthereof, the sensor reuse routine includes triggering an audible and/orvisual alarm notifying the user of improper sensor reuse.

In an embodiment of the twenty-fourth aspect or any other embodimentthereof, the sensor reuse routing includes causing a sensor system tofully or partially shut down and/or cease display of sensor data on auser interface of the sensor system.

In a twenty-fifth aspect, a sensor system is provided configured toimplement the methods of the twenty-fourth aspect or any embodimentsthereof.

In an embodiment of the twenty-fifth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the twenty-fourth aspect or any embodiments thereof.

In a twenty-sixth aspect, a system is provided for determining reuse ofan analyte sensor, comprising, applying a stimulus signal to an analytesensor; measuring a response of the stimulus signal; calculating animpedance based on the response; using a data association function todetermine a correlation of the calculated impedance to one or morerecorded impedance values; and initiating a sensor reuse routine if itis determined that the correlation is above a predetermined threshold.

In an embodiment of the twenty-sixth aspect or any other embodimentthereof, the sensor reuse routine includes triggering an audible and/orvisual alarm notifying the user of improper sensor reuse.

In an embodiment of the twenty-sixth aspect or any other embodimentthereof, the sensor reuse routing includes causing a sensor system tofully or partially shut down and/or cease display of sensor data on auser interface of the sensor system.

In an embodiment of the twenty-sixth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the twenty-fifth aspect.

In a twenty-seventh aspect, a method is provided for applying anoverpotential to an analyte sensor, comprising, applying a stimulussignal to an analyte sensor; measuring a response of the stimulussignal; determining a sensor sensitivity or change in sensor sensitivitybased on the response; and applying an over potential to the sensorbased on the determined sensitivity or sensitivity change.

In an embodiment of the twenty-seventh aspect or any other embodimentthereof, the determining further comprises calculating an impedancebased on the response and determining the sensitivity or sensitivitychange based on the impedance.

In an embodiment of the twenty-seventh aspect or any other embodimentthereof, the determining the sensitivity or sensitivity change furthercomprises correlating the impedance to a predetermined impedance tosensitivity relationship.

In an embodiment of the twenty-seventh aspect or any other embodimentthereof, the applying comprises determining or modifying a length oftime the over potential is applied to the sensor.

In an embodiment of the twenty-seventh aspect or any other embodimentthereof, the applying comprises determining or modifying a magnitude ofthe over potential applied to the sensor.

In a twenty-eighth aspect, a sensor system is provided configured toimplement the methods of the twenty-seventh aspect or any embodimentsthereof.

In an embodiment of the twenty-eighth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the twenty-seventh aspect or any embodiment thereof.

In a twenty-ninth aspect, a method is provided for determining aproperty of a continuous analyte sensor, the method comprising: applyinga stimulus signal to a first analyte sensor having a first workingelectrode and a first reference electrode; measuring a signal responseof the stimulus signal using a second analyte sensor having a secondworking electrode and a second reference electrode; and determining aproperty of the first sensor by correlating the response to apredetermined relationship.

In an embodiment of the twenty-ninth aspect or any other embodimentthereof, the method further comprises generating sensor data by applyinga bias voltage to the first working electrode and measuring a responseto the bias voltage.

In an embodiment of the twenty-ninth aspect or any other embodimentthereof, the method further comprises calibrating the sensor data usingthe determined property.

In an embodiment of the twenty-ninth aspect or any other embodimentthereof, the determined property is one of an impedance and atemperature.

In an embodiment of the twenty-ninth aspect or any other embodimentthereof, the method further comprises determining sensor membrane damageusing the determined property.

In an embodiment of the twenty-ninth aspect or any other embodimentthereof, the method further comprises determining moisture ingress in asensor system encompassing the first and second analyte sensors usingthe determined property.

In a thirtieth aspect, a sensor system is provided configured toimplement the methods of one of the twenty-ninth aspect or anyembodiments thereof.

In an embodiment of the thirtieth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the twenty-ninth aspect or any embodiments thereof.

In a thirty-first aspect, a method is provided for determining a scalingused in a continuous analyte sensor system, the method comprising:applying a first stimulus signal to a first working electrode of ananalyte sensor; measuring a response to the first stimulus signal;applying a second stimulus signal to a second working electrode of theanalyte sensor; measuring a response to the first stimulus signal;determining, using sensor electronics, a scaling factor based on themeasured responses to the first and second stimulus signals; and usingthe scaling factor to generate estimated analyte values based on sensordata generated by the analyte sensor.

In an embodiment of the thirty-first aspect or any other embodimentthereof, the method further comprises the method is performedperiodically.

In an embodiment of the thirty-first aspect or any other embodimentthereof, the determining comprises calculating a first impedance usingthe response to the first stimulus signal and calculating a secondimpedance using the response to the second stimulus signal, and whereinthe scaling factor is a ratio of the first impedance and the secondimpedance.

In an embodiment of the thirty-first aspect or any other embodimentthereof, the first working electrode has a membrane comprising an enzymeconfigured to react with the analyte and the second working electrodehas a membrane does not have the enzyme.

In an embodiment of the thirty-first aspect or any other embodimentthereof, determining the scaling factor comprises updating a previousscaling factor based on the measured responses to the first and secondstimulus signals.

In an embodiment of the thirty-first aspect or any other embodimentthereof, scaling factor is an acetaminophen scaling factor, wherein themethod further comprises updating a further scaling factor based on theacetaminophen scaling factor, and wherein the further scaling factorapplied to the sensor data to generate the estimated analyte values.

In a thirty-second aspect, a sensor system is provided configured toimplement the methods of the thirty-first aspect or any embodimentsthereof.

In an embodiment of the thirty-second aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the thirty-first aspect or any embodiment thereof.

In a thirty-third aspect, a method is provided for calibrating ananalyte sensor, the method comprising: applying a predetermined signalto an analyte sensor; measuring a response to the applied signal;determining, using sensor electronics, a change in impedance associatedwith a membrane of the analyte sensor based on the measured response;calculating a sensitivity change of the analyte sensor based on thedetermined impedance; calculating a corrected sensitivity based on thecalculated sensitivity change and a previously used sensitivity of theanalyte sensor; and generating estimated analyte values using thecorrected sensitivity.

In an embodiment of the thirty-third aspect or any other embodimentthereof, calculating the sensitivity change comprises applying anon-linear compensation function.

In an embodiment of the thirty-third aspect or any other embodimentthereof, the non-linear compensation function is expressed as theequation ΔS=(a*log(t)+b)*ΔI, where ΔS is the change in sensitivity, t isa time since calibration of the analyte sensor, ΔI is the determinedchange in impedance, and a and b are a predetermined coefficients.

In an embodiment of the thirty-third aspect or any other embodimentthereof, a and b are determined by prior testing of similar analytesensors.

In a thirty-fourth aspect, a sensor system is provided configured toimplement the methods of the thirty-third aspect or any embodimentsthereof.

In an embodiment of the thirty-fourth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the thirty-third aspect or any embodiments thereof.

In a thirty-fifth aspect, a method is provided for calibrating ananalyte sensor, comprising: generating sensor data using a subcutaneousanalyte sensor; forming or modifying a conversion function, whereinpre-implant information, internal diagnostic information, and/orexternal reference information are used as inputs to form or modify theconversion function; and calibrating the sensor data using theconversion function.

In an embodiment of the thirty-fifth aspect or any other embodimentthereof, the pre-implant information comprises information selected fromthe group consisting of: a predetermined sensitivity profile associatedwith the analyte sensor, a previously determined relationship between ameasured sensor property and sensor sensitivity, one or more previouslydetermined relationships between a measured sensor property and sensortemperature; sensor data obtained from previously used analyte sensors,a calibration code associated with the analyte sensor, patient specificrelationships between the analyte sensor and one or more of sensitivity,baseline, drift and impedance, information indicative of a site ofsensor implantation; time since manufacture of the analyte sensor, andinformation indicative of the analyte being exposed to temperature orhumidity.

In an embodiment of the thirty-fifth aspect or any other embodimentthereof, the internal diagnostic information comprises informationselected from the group consisting of: stimulus signal output; sensordata indicative of an analyte concentration measured by the sensor;temperature measurements using the sensor or a separate sensor; sensordata generated by a redundant sensor, where the redundant sensor isdesigned to be substantially the same as analyte sensor; sensor datagenerated by an auxiliary sensors, where the auxiliary sensor is havinga different modality as the analyte sensor; a time since the sensor wasimplanted or connected to sensor electronics coupled to the sensor; datarepresentative of a pressure on the sensor or sensor system generated bya pressure sensor; data generated by an accelerometer; a measure ofmoisture ingress; and a measure of noise in an analyte concentrationsignal.

In an embodiment of the thirty-fifth aspect or any other embodimentthereof, the reference information comprises information selected fromthe group consisting of: real-time and/or prior analyte concentrationinformation obtained from a reference monitor, information relating to atype/brand of reference monitor used to provide reference data;information relating to an amount of carbohydrate consumed by a user;information received from a medicament delivery device, glucagonsensitivity information, and information gathered from population baseddata.

In a thirty-sixth aspect, a sensor system is provided configured toimplement the methods of the thirty-fifth aspect or any embodimentsthereof.

In an embodiment of the thirty-sixth aspect or any other embodimentthereof, the sensor system comprises instructions stored in computermemory, wherein the instructions, when executed by one or moreprocessors of the sensor system, cause the sensor system to implementthe method of the thirty-fifth aspect or any embodiment thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages will be appreciated, as theybecome better understood by reference to the following detaileddescription when considered in connection with the accompanyingdrawings, wherein:

FIG. 1A illustrates a schematic diagram of sensor sensitivity as afunction of time during a sensor session, in accordance with oneembodiment; FIG. 1B illustrates schematic diagrams of conversionfunctions at different time periods of a sensor session, in accordancewith the embodiment of FIG. 1A.

FIGS. 2A-2B and FIG. 52C-2D collectively illustrate differentembodiments of processes for generating a sensor sensitivity profile.

FIG. 3A is a Bland-Altman plot illustrating differences between YSIreference measurements and certain in vivo continuous analyte sensorsthat were factory calibrated, in accordance with one embodiment; FIG. 3Bis a Clarke error grid associated with data from the continuous analytesensors associated with FIG. 3A.

FIG. 4 illustrates data from one study that examined the accuracy levelof continuous analyte sensors that accepted one reference measurementabout one hour after insertion into patients, in accordance with oneembodiment.

FIG. 5 illustrates a diagram showing different types of information thatcan be input into the sensor system to define the sensor sensitivityprofile over time

FIG. 6 illustrates a schematic diagram of sensor sensitivity as afunction of time between completion of sensor fabrication and the startof the sensor session, in accordance with one embodiment.

FIG. 7A is a schematic diagram depicting distribution curves of sensorsensitivity corresponding to the Bayesian learning process, inaccordance with one embodiment; FIG. 7B is a schematic diagram depictingconfidence levels, associated with the sensor sensitivity profile, thatcorrespond with the distribution curves shown in FIG. 7A.

FIG. 8 illustrates a graph that provides a comparison between anestimated glucose equivalent baseline and detected glucose equivalentbaseline, in accordance with one study.

FIG. 9 is a schematic of a model sensor circuit in accordance with oneembodiment.

FIG. 10 is a Bode plot of an analyte sensor in accordance with oneembodiment.

FIG. 11 is a flowchart describing a process for determining an impedanceof a sensor in accordance with one embodiment.

FIG. 12 is a flowchart describing a process for determining an impedanceof a sensor based on a derivative response in accordance with oneembodiment.

FIG. 13 is a flowchart describing a process for determining an impedanceof a sensor based on a peak current response in accordance with oneembodiment.

FIG. 14A illustrates a step voltage applied to a sensor and FIG. 14Billustrates a response to the step voltage in accordance with oneembodiment.

FIG. 15 is a flowchart describing a process for estimating analyteconcentration values using a corrected signal based on an impedancemeasurement in accordance with one embodiment.

FIG. 16 is a flowchart describing a process for estimating analyteconcentration values using a predetermined sensitivity profile selectedbased on an impedance measurement in accordance with one embodiment.

FIG. 17 is a flowchart describing a process for determining an errorbased whether a sensitivity determined using an impedance measurementsufficiently corresponds to a predetermined sensitivity in accordancewith one embodiment.

FIG. 18 is a flowchart describing a process for determining atemperature associated with a sensor by correlating an impedancemeasurement to a predetermined temperature-to-impedance relationship inaccordance with one embodiment.

FIG. 19 is a flowchart describing a process for determining moistureegress in sensor electronics associates with an analyte sensoraccordance with one embodiment.

FIG. 20 is a flowchart describing a process for determining membranedamage associated with an analyte sensor in accordance with oneembodiment.

FIG. 21 is a flowchart describing a first process for determining sensorreuse associated in accordance with one embodiment.

FIG. 22 is a flowchart describing a second process for determiningsensor reuse associated in accordance with one embodiment.

FIG. 23 is a schematic of a dual-electrode configuration used todetermine sensor properties in accordance with one embodiment.

FIG. 24 is a diagram of a calibration process that uses various inputsto form a conversion function in accordance with one embodiment.

FIGS. 25-49 collectively illustrate results of studies using stimulussignals to determine sensor properties.

FIG. 50 is a flowchart describing a process for generating estimatedanalyte values using a compensation algorithm based on a measured changein impedance in accordance with one embodiment.

FIGS. 51-53 are graphs of studies comparing use of compensationalgorithms based on measured changes in impedance.

DETAILED DESCRIPTION Definitions

In order to facilitate an understanding of the embodiments describedherein, a number of terms are defined below.

The term “analyte,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is are not to be limited to a special or customized meaning),and refers without limitation to a substance or chemical constituent ina biological fluid (for example, blood, interstitial fluid, cerebralspinal fluid, lymph fluid or urine) that can be analyzed. Analytes mayinclude naturally occurring substances, artificial substances,metabolites, and/or reaction products. In some embodiments, the analytefor measurement by the sensor heads, devices, and methods disclosedherein is glucose. However, other analytes are contemplated as well,including but not limited to acarboxyprothrombin; acylcarnitine; adeninephosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase,hemoglobinopathies, A,S,C,E, D-Punjab, beta-thalassemia, hepatitis Bvirus, HCMV, HIV-1, HTLV-1, Leber hereditary optic neuropathy, MCAD,RNA, PKU, Plasmodium vivax, sexual differentiation, 21-deoxycortisol);desbutylhalofantrine; dihydropteridine reductase; diptheria/tetanusantitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D;fatty acids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I; 17alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins and hormones naturally occurring in blood or interstitialfluids may also constitute analytes in certain embodiments. The analytemay be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte may be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body may also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The terms “continuous analyte sensor,” and “continuous glucose sensor,”as used herein, are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and refer withoutlimitation to a device that continuously or continually measures aconcentration of an analyte/glucose and/or calibrates the device (e.g.,by continuously or continually adjusting or determining the sensor'ssensitivity and background), for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.

The term “biological sample,” as used herein, is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to sample derived from the bodyor tissue of a host, such as, for example, blood, interstitial fluid,spinal fluid, saliva, urine, tears, sweat, or other like fluids.

The term “host,” as used herein, is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to animals, including humans.

The term “membrane system,” as used herein, is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a permeable or semi-permeablemembrane that can be comprised of two or more domains and is typicallyconstructed of materials of a few microns thickness or more, which maybe permeable to oxygen and are optionally permeable to glucose. In oneexample, the membrane system comprises an immobilized glucose oxidaseenzyme, which enables an electrochemical reaction to occur to measure aconcentration of glucose.

The term “domain,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to regions of a membrane that can be layers,uniform or non-uniform gradients (for example, anisotropic), functionalaspects of a material, or provided as portions of the membrane.

The term “sensing region,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the region of a monitoringdevice responsible for the detection of a particular analyte. In oneembodiment, the sensing region generally comprises a non-conductivebody, at least one electrode, a reference electrode and a optionally acounter electrode passing through and secured within the body forming anelectroactive surface at one location on the body and an electronicconnection at another location on the body, and a membrane systemaffixed to the body and covering the electroactive surface.

The term “electroactive surface,” as used herein, is a broad term, andis to be given its ordinary and customary meaning to a person ofordinary skill in the art (and is not to be limited to a special orcustomized meaning), and refers without limitation to the surface of anelectrode where an electrochemical reaction takes place. In oneembodiment, a working electrode measures hydrogen peroxide (H₂O₂)creating a measurable electronic current.

The term “baseline,” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to the component of an analyte sensor signalthat is not related to the analyte concentration. In one example of aglucose sensor, the baseline is composed substantially of signalcontribution due to factors other than glucose (for example, interferingspecies, non-reaction-related hydrogen peroxide, or other electroactivespecies with an oxidation potential that overlaps with hydrogenperoxide). In some embodiments wherein a calibration is defined bysolving for the equation y=mx+b, the value of b represents the baselineof the signal. In certain embodiments, the value of b (i.e., thebaseline) can be zero or about zero. This can be the result of abaseline-subtracting electrode or low bias potential settings, forexample. As a result, for these embodiments, calibration can be definedby solving for the equation y=mx.

The term “inactive enzyme,” as used herein, is a broad term, and is tobe given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an enzyme (e.g., glucoseoxidase, GOx) that has been rendered inactive (e.g., by denaturing ofthe enzyme) and has substantially no enzymatic activity. Enzymes can beinactivated using a variety of techniques known in the art, such as butnot limited to heating, freeze-thaw, denaturing in organic solvent,acids or bases, cross-linking, genetically changing enzymaticallycritical amino acids, and the like. In some embodiments, a solutioncontaining active enzyme can be applied to the sensor, and the appliedenzyme subsequently inactivated by heating or treatment with aninactivating solvent.

The term “non-enzymatic,” as used herein is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to a lack of enzyme activity. Insome embodiments, a “non-enzymatic” membrane portion contains no enzyme;while in other embodiments, the “non-enzymatic” membrane portioncontains inactive enzyme. In some embodiments, an enzyme solutioncontaining inactive enzyme or no enzyme is applied.

The term “substantially,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to being largely but notnecessarily wholly that which is specified.

The term “about,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andwhen associated with any numerical values or ranges, refers withoutlimitation to the understanding that the amount or condition the termsmodify can vary some beyond the stated amount so long as the function ofthe disclosure is realized.

The term “ROM,” as used herein, is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to read-only memory, which is a type of datastorage device manufactured with fixed contents. ROM is broad enough toinclude EEPROM, for example, which is electrically erasable programmableread-only memory (ROM).

The term “RAM,” as used herein, is a broad term, and is to be given itsordinary and customary meaning to a person of ordinary skill in the art(and is not to be limited to a special or customized meaning), andrefers without limitation to a data storage device for which the orderof access to different locations does not affect the speed of access.RAM is broad enough to include SRAM, for example, which is static randomaccess memory that retains data bits in its memory as long as power isbeing supplied.

The term “A/D Converter,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to hardware and/or software thatconverts analog electrical signals into corresponding digital signals.

The terms “raw data stream” and “data stream,” as used herein, are broadterms, and are to be given their ordinary and customary meaning to aperson of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to ananalog or digital signal directly related to the analyte concentrationmeasured by the analyte sensor. In one example, the raw data stream isdigital data in counts converted by an A/D converter from an analogsignal (for example, voltage or amps) representative of an analyteconcentration. The terms broadly encompass a plurality of time spaceddata points from a substantially continuous analyte sensor, whichcomprises individual measurements taken at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes or longer.

The term “counts,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and is not to be limited to a special or customized meaning), andrefers without limitation to a unit of measurement of a digital signal.In one example, a raw data stream measured in counts is directly relatedto a voltage (for example, converted by an A/D converter), which isdirectly related to current from a working electrode.

The term “sensor electronics,” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the components (for example,hardware and/or software) of a device configured to process data. In thecase of an analyte sensor, the data includes biological informationobtained by a sensor regarding the concentration of the analyte in abiological fluid. U.S. Pat. Nos. 4,757,022, 5,497,772 and 4,787,398describe suitable electronic circuits that can be utilized with devicesof certain embodiments.

The term “potentiostat,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to an electrical system thatapplies a potential between the working and reference electrodes of atwo- or three-electrode cell at a preset value and measures the currentflow through the working electrode. The potentiostat forces whatevercurrent is necessary to flow between the working and counter electrodesto keep the desired potential, as long as the needed cell voltage andcurrent do not exceed the compliance limits of the potentiostat.

The term “operably connected,” as used herein, is a broad term, and isto be given its ordinary and customary meaning to a person of ordinaryskill in the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to one or more components beinglinked to another component(s) in a manner that allows transmission ofsignals between the components. For example, one or more electrodes canbe used to detect the amount of glucose in a sample and convert thatinformation into a signal; the signal can then be transmitted to anelectronic circuit. In this case, the electrode is “operably linked” tothe electronic circuit. These terms are broad enough to include wiredand wireless connectivity.

The term “filtering,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to modification of a set of datato make it smoother and more continuous and remove or diminish outlyingpoints, for example, by performing a moving average of the raw datastream.

The term “algorithm,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the computational processes(for example, programs) involved in transforming information from onestate to another, for example using computer processing.

The term “calibration,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the process of determiningthe graduation of a sensor giving quantitative measurements (e.g.,analyte concentration). As an example, calibration may be updated orrecalibrated over time to account for changes associated with thesensor, such as changes in sensor sensitivity and sensor background. Inaddition, calibration of the sensor can involve, automatic,self-calibration, e.g., without using reference analyte values afterpoint of use.

The terms “sensor data,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to data received from acontinuous analyte sensor, including one or more time-spaced sensor datapoints.

The terms “reference analyte values” and “reference data,” as usedherein, are broad terms, and are to be given their ordinary andcustomary meaning to a person of ordinary skill in the art (and are notto be limited to a special or customized meaning), and refer withoutlimitation to reference data from a reference analyte monitor, such as ablood glucose meter, or the like, including one or more reference datapoints. In some embodiments, the reference glucose values are obtainedfrom a self-monitored blood glucose (SHBG) test (for example, from afinger or forearm blood test) or a YSI (Yellow Springs Instruments)test, for example.

The terms “interferents” and “interfering species,” as used herein, arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and are not to be limited to aspecial or customized meaning), and refer without limitation to effectsand/or species that interfere with the measurement of an analyte ofinterest in a sensor to produce a signal that does not accuratelyrepresent the analyte measurement. In one example of an electrochemicalsensor, interfering species are compounds with an oxidation potentialthat overlaps with the analyte to be measured, producing a falsepositive signal.

The term “sensor session,” as used herein, is a broad term, and is to begiven its ordinary and customary meaning to a person of ordinary skillin the art (and is not to be limited to a special or customizedmeaning), and refers without limitation to the period of time the sensoris applied to (e.g. implanted in) the host or is being used to obtainsensor values. For example, in some embodiments, a sensor sessionextends from the time of sensor implantation (e.g. including insertionof the sensor into subcutaneous tissue and placing the sensor into fluidcommunication with a host's circulatory system) to the time when thesensor is removed.

The terms “sensitivity” or “sensor sensitivity,” as used herein, arebroad terms, and are to be given their ordinary and customary meaning toa person of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and refer without limitation to anamount of signal produced by a certain concentration of a measuredanalyte, or a measured species (e.g., H₂O₂) associated with the measuredanalyte (e.g., glucose). For example, in one embodiment, a sensor has asensitivity of from about 1 to about 300 picoAmps of current for every 1mg/dL of glucose analyte.

The term “sensitivity profile” or “sensitivity curve,” as used herein,are broad terms, and are to be given their ordinary and customarymeaning to a person of ordinary skill in the art (and is not to belimited to a special or customized meaning), and refer withoutlimitation to a representation of a change in sensitivity over time

Overview

Conventional in vivo continuous analyte sensing technology has typicallyrelied on reference measurements performed during a sensor session forcalibration of the continuous analyte sensor. The reference measurementsare matched with substantially time corresponding sensor data to creatematched data pairs. Regression is then performed on the matched datapairs (e.g., by using least squares regression) to generate a conversionfunction that defines a relationship between a sensor signal and anestimated glucose concentration.

In critical care settings, calibration of continuous analyte sensors isoften performed by using, as reference, a calibration solution with aknown concentration of the analyte. This calibration procedure can becumbersome, as a calibration bag, separate from (and an addition to) anIV (intravenous) bag, is typically used. In the ambulatory setting,calibration of continuous analyte sensors has traditionally beenperformed by capillary blood glucose measurements (e.g., a finger stickglucose test), through which reference data is obtained and input intothe continuous analyte sensor system. This calibration proceduretypically involves frequent finger stick measurements, which can beinconvenient and painful.

Heretofore, systems and methods for in vitro calibration of a continuousanalyte sensor by the manufacturer (e.g., factory calibration), withoutreliance on periodic recalibration, have for the most part beeninadequate with respect to high levels of sensor accuracy. Part of thiscan be attributed to changes in sensor properties (e.g., sensorsensitivity) that can occur during sensor use. Thus, calibration ofcontinuous analyte sensors has typically involved periodic inputs ofreference data, whether they are associated with a calibration solutionor with a finger stick measurement. This can be very burdensome to thepatient in the ambulatory setting or the hospital staff in the criticalcare setting.

Described herein are continuous analyte sensors that are capable ofcontinuous, automatic self-calibration during a sensor session andcapable of achieving high levels of accuracy, without (or with reduced)reliance on reference data from a reference analyte monitor (e.g., froma blood glucose meter). In some embodiments, the continuous analytesensor is an invasive, minimally invasive, or non-invasive device. Thecontinuous analyte sensor can be a subcutaneous, transdermal, orintravascular device. In certain embodiments, one or more of thesedevices may form a continuous analyte sensor system. For instance, thecontinuous analyte sensor system may be comprised of a combination of asubcutaneous device and a transdermal device, a combination of asubcutaneous device and an intravascular device, a combination of atransdermal device and an intravascular device, or a combination of asubcutaneous device, a transdermal device, and an intravascular device.In some embodiments, the continuous analyte sensor can analyze aplurality of intermittent biological samples (e.g., blood samples). Thecontinuous analyte sensor can use any glucose-measurement method,including methods involving enzymatic, chemical, physical,electrochemical, spectrophotometric, polarimetric, calorimetric,iontophoretic, and radiometric mechanisms, and the like.

In certain embodiments, the continuous analyte sensor includes one ormore working electrodes and one or more reference electrode, whichoperate together to measure a signal associated with a concentration ofthe analyte in the host. The output signal from the working electrode istypically a raw data stream that is calibrated, processed, and used togenerate an estimated analyte (e.g., glucose) concentration. In certainembodiments, the continuous analyte sensor may measure an additionalsignal associated with the baseline and/or sensitivity of the sensor,thereby enabling monitoring of baseline and/or additional monitoring ofsensitivity changes or drift that may occur in a continuous analytesensor over time.

In some embodiments, the sensor extends through a housing, whichmaintains the sensor on the skin and provides for electrical connectionof the sensor to sensor electronics. In one embodiment, the sensor isformed from a wire. For example, the sensor can include an elongatedconductive body, such as a bare elongated conductive core (e.g., a metalwire) or an elongated conductive core coated with one, two, three, four,five, or more layers of material, each of which may or may not beconductive. The elongated sensor may be long and thin, yet flexible andstrong. For example, in some embodiments the smallest dimension of theelongated conductive body is less than about 0.1 inches, 0.075 inches,0.05 inches, 0.025 inches, 0.01 inches, 0.004 inches, or 0.002 inches.Other embodiments of the elongated conductive body are disclosed in U.S.Patent Application Publication No. 2011/0027127, which is incorporatedherein by reference in its entirety. Preferably, a membrane system isdeposited over at least a portion of electroactive surfaces of thesensor 102 (including a working electrode and optionally a referenceelectrode) and provides protection of the exposed electrode surface fromthe biological environment, diffusion resistance (limitation) of theanalyte if needed, a catalyst for enabling an enzymatic reaction,limitation or blocking of interferants, and/or hydrophilicity at theelectrochemically reactive surfaces of the sensor interface. Disclosuresregarding the different membrane systems that may be used with theembodiments described herein are described in U.S. Patent PublicationNo. US-2009-0247856-A1, which is incorporated herein by reference in itsentirety.

Calibrating sensor data from continuous analyte sensors generallyinvolves defining a relationship between sensor-generated measurements(e.g., in units of nA or digital counts after A/D conversion) and one ormore reference measurement (e.g., in units of mg/dL or mmol/L). Incertain embodiments, one or more reference measurements obtained shortlyafter the analyte sensor is manufactured, and before sensor use, areused for calibration. The reference measurement may be obtained in manyforms. For example, in certain embodiments, the reference measurementmay be determined from a ratio or correlation between the sensitivity ofa sensor (e.g., from a certain sensor lot) with respect to in vivoanalyte concentration measurements and the sensitivity of another sensor(e.g., from the same lot made in substantially the same way undersubstantially same conditions) with respect to in vitro analyteconcentration measurements at a certain time period. By providing acontinuous analyte sensor with a predetermined in vivo to in vitro ratioand a predetermined sensitivity profile (as described in more detailelsewhere herein), self-calibration of the sensor can be achieved inconjunction with high levels of sensor accuracy.

With self-calibration, the need for recalibration, by using referencedata during a sensor session, may be eliminated, or else lessened, suchthat recalibration may be called for only in certain limitedcircumstances, such as when sensor failure is detected. Additionally oralternatively, in some embodiments, the continuous analyte sensor may beconfigured to request and accept one or more reference measurements(e.g., from a finger stick glucose measurement or a calibrationsolution) at the start of the sensor session. In some embodiments, useof a reference measurement at the start of the sensor session inconjunction with a predetermined sensor sensitivity profile caneliminate or substantially reduce the need for further referencemeasurements.

With certain implantable enzyme-based electrochemical glucose sensors,the sensing mechanism depends on certain phenomena that have a generallylinear relationship with glucose concentration, for example: (1)diffusion of an analyte through a membrane system situated between animplantation site (e.g., subcutaneous space) and an electroactivesurface, (2) rate of an enzyme-catalyzed reaction of the analyte toproduce a measured species within the membrane system (e.g., the rate ofa glucose oxidase-catalyzed reaction of glucose with O₂ which producesgluconic acid and H₂O₂), and (3) diffusion of the measured species(e.g., H₂O₂) to the electroactive surface. Because of this generallylinear relationship, calibration of the sensor is obtained by solvingthe equation:

y=mx+b

wherein y represents the sensor signal (counts), x represents theestimated glucose concentration (mg/dL), m represents the sensorsensitivity to analyte concentration (counts/mg/dL), and b representsthe baseline signal (counts). As described elsewhere herein, in certainembodiments, the value b (i.e., the baseline) can be zero or about zero.As a result, for these embodiments, calibration can be defined bysolving for the equation y=mx.

In some embodiments, the continuous analyte sensor system is configuredto estimate changes or drift in sensitivity of the sensor for an entiresensor session as a function of time (e.g., elapsed time since start ofthe sensor session). As described elsewhere herein, this sensitivityfunction plotted against time may resemble a curve. Additionally oralternatively, the system can also be configured to determine sensorsensitivity changes or drift as a function of time and one or more otherparameters that can also affect sensor sensitivity or provide additionalinformation about sensor sensitivity. These parameters can affect sensorsensitivity or provide additional information about sensor sensitivityprior to the sensor session, such as parameters associated with thesensor fabrication (e.g., materials used to fabricate sensor membrane,the thickness of the sensor membrane, the temperature at which thesensor membrane was cured, the length of time the sensor was dipped in aparticular coating solution, etc.). In certain embodiments, some of theparameters involve information, obtained prior to the sensor session,which can be accounted for in a calibration code that is associated witha particular sensor lot. Other parameters can be associated withconditions surrounding the sensor after its manufacture, but before thesensor session, such as, for example, the level of exposure of thesensor to certain levels of humidity or temperature while the sensor isin a package in transit from the manufacturing facility to the patient.Yet other parameters (e.g., sensor membrane permeability, temperature atthe sample site, pH at the sample site, oxygen level at the sample site,etc.) can affect sensor sensitivity or provide additional informationabout sensor sensitivity during the sensor session.

Determination of sensor sensitivity at different times of a sensorsession based on the predetermined sensor sensitivity profile can beperformed prior to the sensor session or at the start of the sensorsession. Additionally, in certain embodiments, determination of sensorsensitivity, based on the sensor sensitivity profile, can becontinuously adjusted to account for parameters that affect sensorsensitivity or provide additional information about sensor sensitivityduring the sensor session. These determinations of sensor sensitivitychange or drift can be used to provide self-calibration, updatecalibration, supplement calibration based on measurements (e.g., from areference analyte monitor), and/or validate or reject reference analytemeasurements from a reference analyte monitor. In some embodiments,validation or rejection of reference analyte measurements can be basedon whether the reference analyte measurements are within a range ofvalues associated with the predetermined sensor sensitivity profile.

Some of the continuous analyte sensors described herein may beconfigured to measure a signal associated with a non-analyte constant inthe host. Preferably, the non-analyte constant signal is measuredbeneath the membrane system on the sensor. In one example of acontinuous glucose sensor, a non-glucose constant that can be measuredis oxygen. In some embodiments, a change in oxygen transport, which canbe indicative of a change or drift in the sensitivity of the glucosesignal, can be measured by switching the bias potential of the workingelectrode, an auxiliary oxygen-measuring electrode, an oxygen sensor, orthe like.

Additionally, some of the continuous analyte sensors described hereinmay be configured to measure changes in the amount of background noisein the signal. Detection of changes which exceed a certain threshold canprovide the basis for triggering calibration, updating calibration,and/or validating or rejecting inaccurate reference analyte values froma reference analyte monitor. In one example of a continuous glucosesensor, the background noise is composed substantially of signalcontribution from factors other than glucose (for example, interferingspecies, non-reaction-related hydrogen peroxide, or other electroactivespecies with an oxidation potential that overlaps with hydrogenperoxide). Namely, the continuous glucose sensor is configured tomeasure a signal associated with the baseline (which includessubstantially all non-glucose related current generated), as measured bythe sensor in the host. In some embodiments, an auxiliary electrodelocated beneath a non-enzymatic portion of the membrane system is usedto measure the baseline signal. The baseline signal can be subtractedfrom the glucose+baseline signal to obtain a signal associated entirelyor substantially entirely with glucose concentration. Subtraction may beaccomplished electronically in the sensor using a differentialamplifier, digitally in the receiver, and/or otherwise in the hardwareor software of the sensor or receiver as described in more detailelsewhere herein.

Together, by determining sensor sensitivity based on a sensitivityprofile and by measuring a baseline signal, the continuous analytesensor can be continuously self-calibrated during a sensor sessionwithout (or with reduced) reliance on reference measurements from areference analyte monitor or calibration solution.

Determination of Sensor Sensitivity

As described elsewhere herein, in certain embodiments, self-calibrationof the analyte sensor system can be performed by determining sensorsensitivity based on a sensitivity profile (and a measured or estimatedbaseline), so that the following equation can be solved:

y=mx+b

wherein y represents the sensor signal (counts), x represents theestimated glucose concentration (mg/dL), m represents the sensorsensitivity to the analyte (counts/mg/dL), and b represents the baselinesignal (counts). From this equation, a conversion function can beformed, whereby a sensor signal is converted into an estimated glucoseconcentration.

It has been found that a sensor's sensitivity to analyte concentrationduring a sensor session will often change or drift as a function oftime. FIG. 1A illustrates this phenomenon and provides a plot of sensorsensitivities 110 of a group of continuous glucose sensors as a functionof time during a sensor session. FIG. 1B provides three plots ofconversion functions at three different time periods of a sensorsession. As shown in FIG. 1B, the three conversion functions havedifferent slopes, each of which correspond to a different sensorsensitivity. Accordingly, the differences in slopes over time illustratethat changes or drift in sensor sensitivity occur over a sensor session.

Referring back to the study associated with FIG. 1A, the sensors weremade in substantially the same way under substantially the sameconditions. The sensor sensitivities associated with the y-axis of theplot are expressed as a percentage of a substantially steady statesensitivity that was reached about three days after start of the sensorsession. In addition, these sensor sensitivities correspond tomeasurements obtained from YSI tests. As shown in the plot, thesensitivities (expressed as a percentage of a steady state sensitivity)of each sensor, as measured, are very close to sensitivities of othersensors in the group at any given time of the sensor session. While notwishing to be bound by theory, it is believed that the observed upwardtrend in sensitivity (over time), which is particularly pronounced inthe early part of the sensor session, can be attributed to conditioningand hydration of sensing regions of the working electrode. It is alsobelieved that the glucose concentration of the fluid surrounding thecontinuous glucose sensor during startup of the sensor can also affectthe sensitivity drift.

With the sensors tested in this study, the change in sensor sensitivity(expressed as a percentage of a substantially steady state sensitivity),over a time defined by a sensor session, resembled a logarithmic growthcurve. It should be understood that other continuous analyte sensorsfabricated with different techniques, with different specifications(e.g., different membrane thickness or composition), or under differentmanufacturing conditions, may exhibit a different sensor sensitivityprofile (e.g., one associated with a linear function). Nonetheless, withimproved control over operating conditions of the sensor fabricationprocess, high levels of reproducibility have been achieved, such thatsensitivity profiles exhibited by individual sensors of a sensorpopulation (e.g., a sensor lot) are substantially similar and sometimesnearly identical.

It has been discovered that the change or drift in sensitivity over asensor session is not only substantially consistent among sensorsmanufactured in substantially the same way under substantially sameconditions, but also that modeling can be performed through mathematicalfunctions that can accurately estimate this change or drift. Asillustrated in FIG. 1A, an estimative algorithm function 120 can be usedto define the relationship between time during the sensor session andsensor sensitivity. The estimative algorithm function may be generatedby testing a sample set (comprising one or more sensors) from a sensorlot under in vivo and/or in vitro conditions. Alternatively, theestimative algorithm function may be generated by testing each sensorunder in vivo and/or in vitro conditions.

In some embodiments, a sensor may undergo an in vitro sensor sensitivitydrift test, in which the sensor is exposed to changing conditions (e.g.,step changes of glucose concentrations in a solution), and an in vitrosensitivity profile of the sensor is generated over a certain timeperiod. The time period of the test may substantially match an entiresensor session of a corresponding in vivo sensor, or it may encompass aportion of the sensor session (e.g., the first day, the first two days,or the first three days of the sensor session, etc.). It is contemplatedthat the above-described test may be performed on each individualsensor, or alternatively on one or more sample sensors of a sensor lot.From this test, an in vitro sensitivity profile may be created, fromwhich an in vivo sensitivity profile may be modeled and/or formed.

From the in vivo or in vitro testing, one or more data sets, eachcomprising data points associating sensitivity with time, may begenerated and plotted. A sensitivity profile or curve can then be fittedto the data points. If the curve fit is determined to be satisfactory(e.g., if the standard deviation of the generated data points is less acertain threshold), then the sensor sensitivity profile or curve may bejudged to have passed a quality control and suitable for release. Fromthere, the sensor sensitivity profile can be transformed into anestimative algorithm function or alternatively into a look-up table. Thealgorithm function or look-up table can be stored in a computer-readablememory, for example, and accessed by a computer processor.

The estimative algorithm function may be formed by applying curvefitting techniques that regressively fit a curve to data points byadjusting the function (e.g., by adjusting constants of the function)until an optimal fit to the available data points is obtained. Simplyput, a “curve” (i.e., a function sometimes referred to as a “model”) isfitted and generated that relates one data value to one or more otherdata values and selecting parameters of the curve such that the curveestimates the relationship between the data values. By way of example,selection of the parameters of the curve may involve selection ofcoefficients of a polynomial function. In some embodiments, the curvefitting process may involve evaluating how closely the curve determinedin the curve fitting process estimates the relationship between the datavalues, to determine the optimal fit. The term “curve,” as used herein,is a broad term, and is to be given its ordinary and customary meaningto a person of ordinary skill in the art (and is not to be limited to aspecial or customized meaning), and refers to a function or a graph of afunction, which can involve a rounded curve or a straight curve, i.e., aline.

The curve may be formed by any of a variety of curve fitting techniques,such as, for example, the linear least squares fitting method, thenon-linear least squares fitting method, the Nelder-Mead Simplex method,the Levenberg-Marquardt method, and variations thereof. In addition, thecurve may be fitted using any of a variety of functions, including, butnot limited to, a linear function (including a constant function),logarithmic function, quadratic function, cubic function, square rootfunction, power function, polynomial function, rational function,exponential function, sinusoidal function, and variations andcombinations thereof. For example, in some embodiments, the estimativealgorithm comprises a linear function component which is accorded afirst weight w1, a logarithmic function component which is accorded asecond weight w2, and an exponential function component which isaccorded a third weight w3. In further embodiments, the weightsassociated with each component can vary as a function of time and/orother parameters, but in alternative embodiment, one or more of theseweights are constant as a function of time.

In certain embodiments, the estimative algorithm function's correlation(e.g., R2 value), which is a measure of the quality of the fit of thecurve to the data points, with respect to data obtained from the samplesensors, may be one metric used to determine whether a function isoptimal. In certain embodiments, the estimative algorithm functionformed from the curve fitting analysis may be adjusted to account forother parameters, e.g., other parameters that may affect sensorsensitivity or provide additional information about sensor sensitivity.For example, the estimative algorithm function may be adjusted toaccount for the sensitivity of the sensor to hydrogen peroxide or otherchemical species.

Estimative algorithms formed and used to accurately estimate anindividual sensor's sensitivity, at any time during a sensor session,can be based on factory calibration and/or based on a single earlyreference measurement (e.g., using a single point blood glucosemonitor). In some embodiments, sensors across a population of continuousanalyte sensors manufactured in substantially the same way undersubstantially same conditions exhibit a substantially fixed in vivo toin vitro sensitivity relationship. For example, in one embodiment, thein vivo sensitivity of a sensor at a certain time after start of sensoruse (e.g., at t=about 5, 10, 15, 30, 60, 120, or 180 minutes aftersensor use) is consistently equal to a measured in vitro sensitivity ofthe sensor or of an equivalent sensor. From this relationship, aninitial value of in vivo sensitivity can be generated, from which analgorithmic function corresponding to the sensor sensitivity profile canbe formed. Put another way, from this initial value (which representsone point in the sensor sensitivity profile), the rest of the entiresensor sensitivity profile can be determined and plotted. The initialvalue of in vivo sensitivity can be associated with any portion of thesensor sensitivity profile. In certain embodiments, multiple initialvalues of in vivo sensitivities, which are time-spaced apart, and whichcorrespond to multiple in vitro sensitivities, can be calculated andcombined together to generate the sensor sensitivity profile.

In some embodiments, as illustrated in FIG. 2A, the initial value 210 ofin vivo sensitivity is associated with a time corresponding to the start(near the start) of the sensor session. As illustrated in FIG. 2B, basedon this initial value 210, the rest of the sensor sensitivity profile220 is plotted (i.e., plotted forward and backward across the x-axiscorresponding to time). However, as illustrated in FIG. 2C, in someembodiments, the initial value 210′ may be associated with any othertime of the sensor session. For example, as illustrated in FIG. 2C, inone embodiment, the in initial value 210′ of in vivo sensitivity isassociated with a time (e.g., at about day 3) when the sensitivity hassubstantially reached steady state. From the initial value 210′, therest of the sensor sensitivity profile 220′ is plotted, as illustratedin FIG. 2D.

With other embodiments, although the in vivo to in vitro sensitivityrelationship was not equal, the relationship nonetheless involved aconsistently fixed ratio. By having a substantially fixed in vivo to invitro sensitivity relationship, some of the sensors described herein canbe factory calibrated by evaluating the in vitro sensitivitycharacteristic (e.g., one or more sensitivity values measured at certaintime periods) of a sensor from a particular sensor lot at amanufacturing facility, defining the in vivo sensitivity characteristicof other sensors in the same sensor lot based on its relationship withthe measured in vitro sensitivity characteristic, and storing thiscalculated in vivo sensitivity characteristic onto electronicsassociated with the sensors (e.g., in computer memory of a sensorelectronics, discussed more elsewhere herein, configured to be operablycoupled to the sensor during sensor use).

Accordingly, with information obtained prior to the sensor sessionrelating to an in vivo to in vitro sensor sensitivity relationship and apredetermined sensor sensitivity profile, factory calibration isachieved in conjunction with high levels of sensor accuracy. Forexample, in some embodiments, the sensor was capable of achieving anaccuracy corresponding to a mean absolute relative difference of no morethan about 10% over a sensor session of at least about 3 days, andsometimes at least about 4, 5, 6, 7, or 10 days. In some embodiments,the sensor was capable of achieving an accuracy, over a over a sensorsession of at least about 3 days, corresponding to a mean absoluterelative difference of no more than about 7%, 5%, or 3%. With factorycalibration, the need for recalibration may be eliminated, or elserequired only in certain circumstances, such as in response to detectionof sensor failure.

With reference back to the study associated with FIG. 1A, the sensorswere built with a working electrode configured to measure aglucose+baseline signal and a corresponding auxiliary electrodeconfigured to measure only the baseline signal. Sensor electronics inthe sensor system subtracted the baseline signal from theglucose+baseline signal to obtain a signal associated entirely orsubstantially entirely to glucose concentration. In addition, analgorithmic function was generated and stored in sensor electronicsassociated with the sensors to estimate the sensitivity of these sensorsduring their lives. This algorithmic function is plotted in FIG. 1A andshown closely overlying the measured sensor sensitivities of thesensors. With the determination of baseline and sensitivity at any giventime during the life of a sensor, a conversion function is formed,whereby a sensor signal is converted into an estimated glucoseconcentration.

As illustrated in FIG. 3A, which is a Bland-Altman plot showingdifferences between YSI reference measurements and certain in vivocontinuous analyte sensors that were factory calibrated, themeasurements from these sensors exhibited very high accuracy. The linesin FIG. 3A represent accuracy standards corresponding to a deviationfrom actual measured values (using YSI tests) of less than ±10 mg/dL atglucose concentrations between about 40 and 75 mg/dL, and less than ±15%at glucose concentrations between about 75 mg/dL and 400 mg/dL. Indeed,the difference between estimated glucose concentration values, which arecalculated using a predetermined sensor sensitivity profile, and actualmeasured values (using YSI tests) over a sensor life, differed by nomore than about 10 mg/dL at glucose concentrations between about 40 and75 mg/dL, and no more than 15% at glucose concentrations between about75 mg/dL and 400 mg/dL. Furthermore, at glucose concentrations betweenabout 40 mg/dL and 75 mg/dL, about 97% of the estimated glucoseconcentrations values were within ±5 mg/dL of corresponding YSI measuredvalues, and at glucose concentrations between about 70 mg/dL and 400mg/dL, about 99% of the estimated glucose concentrations were within±10% of corresponding YSI measured values.

FIG. 3B illustrates a Clarke error grid associated with the factorycalibration study associated with FIG. 3A. The Clarke error grid of FIG.3B is a Clarke error grid is based on a correlation plot of theperformance of the above-described factory calibration method withrespect to a reference method in the form of YSI measurements. If thecorrelation was perfect, all points would fall on a 45° line. The areasurrounding this line is divided into zones that predict the clinicalconsequences in terms of action taken by the patient, depending on wherethe measurements by the factory calibration method fall off the line.Zone A corresponds to a clinically accurate decision (e.g., takeinsulin, take glucose, or do nothing), zone B a clinically acceptabledecision, and zone D corresponds to a clinically erroneous decision. Asshown in FIG. 3B, all of data points from the factory calibration studyfell within either zone A or zone B. In fact, almost all of the datapoints fell within Zone A, thus establishing that the above-describedfactory calibration study provided very accurate glucose concentrationmeasurements.

While individual sensors of a sensor group manufactured undersubstantially identical conditions have been found to generally exhibita substantially similar or a nearly identical sensor sensitivity profileand have a substantially similar or a nearly identical in vivo to invitro sensor sensitivity relationship, it has been found that at timesthe actual sensor sensitivity (i.e., sensitivity expressed as an actualsensitivity value, and not as a percentage of a substantially steadystate sensitivity) can vary between sensors. For example, even thoughindividual sensors may have been manufactured under substantiallyidentical conditions, they can have different sensitivitycharacteristics during use if they are exposed to different environmentconditions (e.g., exposure to radiation, extreme temperature, abnormaldehydration conditions, or any environment that can damage the enzyme inthe sensor membrane or other parts of the sensor, etc.) during the timeperiod between sensor fabrication and sensor use.

Accordingly, to compensate for potential effects resulting from theseconditions, in certain embodiments, the continuous analyte sensors areconfigured to request and accept one or more reference measurements(e.g., from a finger stick glucose measurement or from a calibrationsolution) at the start of the sensor session. For example, the requestfor one or more reference measurements can be made at about 15 minutes,30 minutes, 45 minutes, 1 hour, 2 hours, 3 hours, etc., after activationof the sensor. In some embodiments, sensor electronics are configured toprocess and use the reference data to generate (or adjust) a sensorsensitivity profile in response to the input of one or more referencemeasurements into the sensor. For example, if a reference measurement ofglucose concentration is taken and input into the sensor at time=x, analgorithmic function of sensor sensitivity can be generated by matchingthe sensor sensitivity profile at time=x with the reference measurement.Use of the one of the one or more reference measurements at the start ofthe sensor in conjunction with a predetermined sensor sensitivityprofile permits self-calibration of the sensor without or with a reducedneed for further reference measurements.

FIG. 4 illustrates a Bland-Altman plot showing differences between YSIreference measurements and in vivo continuous analyte sensors thataccepted one reference measurement about one hour after insertion intopatients. The lines in FIG. 4 represent accuracy standards correspondingto a deviation from actual measured values (using YSI tests) of lessthan about ±20 mg/dL at glucose concentrations between about 40 mg/dLand 75 mg/dL, and less than about ±20% at glucose concentrations betweenabout 75 mg/dL and 400 mg/dL. From this reference measurement, aninitial value of in vivo sensor sensitivity was generated, which inturned allow for the formation of an algorithmic function correspondingto the sensitivity profile for the rest of the sensor session. Thesensors were built with a working electrode and an auxiliary electrodeused to measure a baseline signal, which was subtracted from theglucose+baseline signal obtained by the working electrode. As shown,about 85% of the estimated glucose concentrations were within the range410, defined as ±20 mg/dL from corresponding YSI measured values forglucose concentrations between about 40 mg/dL and 75 mg/dL and ±20% fromcorresponding YSI measured values for glucose concentrations betweenabout 75 mg/dL and 400 mg/dL. Additionally, at glucose concentrationsbetween about 40 mg/dL and 75 mg/dL, about 95% of the estimated glucoseconcentrations values were within ±20 mg/dL of corresponding YSImeasured values. The sensors in this study obtained an overall accuracylevel corresponding to a Mean Absolute Relative Difference of about 12%over a sensor session of at least seven days, and a first-day accuracylevel corresponding to a Mean Absolute Relative Difference of about 11%.The Median Absolute Relative Difference obtained were about 10% for bothoverall and fist-day accuracy levels.

FIG. 5 is a diagram illustrating different types of information that canbe input into the sensor system to define the sensor sensitivity profileover time, in one embodiment. Input information can include informationobtained prior to the sensor session 510 and information obtained duringthe sensor session 520. In the embodiment depicted in FIG. 5, bothinformation obtained prior to the sensor session 510 and informationobtained during the sensor session 520 are used to generate, adjust, orupdate a function 530 associated with the sensor sensitivity profile,but in another embodiment, the sensor system may be configured to useonly information obtained prior to the sensor session. In certainembodiments, formation of an initial sensor sensitivity profile canoccur prior to the sensor session, at the start of the sensor session,or shortly after the start of the sensor session. Additionally, incertain embodiments, the sensor sensitivity profile can be continuouslyadjusted, regenerated, or updated to account for parameters that mayaffect sensor sensitivity or provide additional information about sensorsensitivity during the sensor session. Information obtained prior to thesensor session can include, for example, the sensor sensitivity profilethat is generated before or at the start of the sensor session, aspreviously described. It can also include a sensitivity value associatedwith a substantially fixed in vivo to in vitro sensor sensitivityrelationship, as previously described.

Alternatively, instead of a fixed sensitivity value, the in vivo to invitro sensor sensitivity relationship may be defined as a function oftime between completion of sensor fabrication (or the time calibrationcheck was performed on sensors from the same lot) and the start of thesensor session. As shown in FIG. 6, it has been discovered that asensor's sensitivity to analyte concentration can change as a functionof time between completion of sensor fabrication and the start of thesensor session. FIG. 6 illustrates this phenomenon through a plot, whichresembles a downward trend in sensitivity over time between completionof sensor fabrication and the start of the sensor session. Similar tothe discovered change or drift in sensitivity over time of a sensorsession, this change or drift in sensitivity over time betweencompletion of sensor fabrication and the start of the sensor session isgenerally consistent among sensors that have not only been manufacturedin substantially the same way under substantially same conditions, butthat also have avoided exposure to certain conditions (e.g., exposure toradiation, extreme temperature, abnormal dehydration conditions, or anyenvironment that can damage the enzyme in the sensor membrane or otherparts of the sensor, etc.) Accordingly, the change or drift insensitivity over time between completion of sensor fabrication and thestart of the sensor session can also be modeled through a mathematicalfunction 620 that accurately estimates this change or drift. Theestimative algorithm function 620 may be any of a variety of functions,such as, for example, a linear function (including a constant function),logarithmic function, quadratic function, cubic function, square rootfunction, power function, polynomial function, rational function,exponential function, sinusoidal function, and combinations thereof.

Information obtained prior to the sensor session can also includeinformation relating to certain sensor characteristics or properties. Byway of example and not to be limiting, information obtained prior to thesensor session may include the particular materials used to fabricatethe sensor (e.g., materials used to form the sensor membrane), thethickness of the sensor membrane, the membrane's permeability to glucoseor other chemical species, the in vivo or in vitro sensor sensitivityprofile of another sensor made in substantially the same way undersubstantially same conditions, etc. In certain embodiments, informationobtained prior to the sensor session can include information relating tothe process conditions under which the sensor is fabricated. Thisinformation can include, for example, the temperature at which thesensor membrane was cured, the length of time the sensor was dipped in aparticular coating solution, etc. In other embodiments, informationobtained prior to the sensor session can relate to patient physiologicalinformation. For example, the patient's age, body mass index, gender,and/or historic patient sensitivity profiles, can be used as parametersto form the sensor sensitivity profile. Other information obtained priorto the sensor session that may also be used includes informationrelating to sensor insertion, such as, for example, location (e.g.,abdomen vs. back) or depth of sensor insertion.

In general, the sensor sensitivity functions can be created bytheoretical or empirical methods, or both, and stored as functions or aslook-up-tables, thereby allowing for sensor self-calibration thateliminates (or substantially reduces) the need for referencemeasurements. The sensor sensitivity functions can be generated at themanufacturing facility and shipped with the system or generated by thesystem shortly prior to (or during) use. The term “self-calibration,” asused herein, is a broad term, and is to be given its ordinary andcustomary meaning to a person of ordinary skill in the art (and is notto be limited to a special or customized meaning), and refers withoutlimitation to calibration of a sensor or device which is performed by acontrol system manufacturer or installer, the sensor manufacturer, oranyone other than the user of the sensor. The sensor calibration can beperformed on the individual sensor on which the control system isinstalled, or it can be performed on a reference sensor, for example onefrom the same sensor lot, and the calibration functions can betransferred from one control system to another. In some embodiments,systems may be shipped with some self-calibration functions and thenhave others added by the sensor user. Also, sensor systems may beshipped with self-calibration functions and only need adjustments ortouch-up calibrations during use.

In certain embodiments, the sensor sensitivity profile may be adjustedduring sensor use to account for, in real-time, certain parameters thatmay affect sensor sensitivity or provide additional information aboutsensor sensitivity. These parameters may include, but are not limitedto, parameters associated with sensor properties, such as, for example,sensor membrane permeability or the level of sensor hydration, orparameters associated with patient physiological information, such as,for example, patient temperature (e.g., temperature at the sample siteor skin temperature), pH at the sample site, hematocrit level, or oxygenlevel at the sample site. In some embodiments, the continuous analytesensor may be fitted with a thermistor on an ex vivo portion of thesensor and a thermal conductive line that extends from the thermistor tothe sample site.

In some embodiments, calibration methods can be improved byalgorithmically quantifying each unique sensor/user environment.Quantification can be achieved by generation or adjustment of thesensitivity profile, which may involve an inference engine involving acausal probabilistic network such as a Bayesian network. A Bayesiannetwork includes a conditional probability-based network that relies onthe Bayes theorem to characterize the likelihood of different outcomesbased on known prior probabilities (e.g., prevalence of observed in vivosensitivities under certain parameter conditions) and newly acquiredinformation (e.g., occurrence or nonoccurrence of the aforementionedconditions).

In certain embodiments, quantification is achieved through theapplication of an analytical Bayesian framework to data already existingwithin the system algorithm. Quantification can involve: (1) wedge(e.g., maximum or minimum) values for parameters related to sensitivityand baseline; (2) sensitivity values calculated during a sensor session;and (3) baselines calculated during the sensor session. In someembodiments, the first set of wedge values are based on parameterdistributions of “prior” data (e.g., data collected from previousclinical studies). By using a Bayesian network, a system can learn fromdata collected during the sensor session, and adapt the wedge values (orother algorithm parameters and/or constraints) to a particularsensor/user environment. In turn, the system's calibration method can beimproved to have better accuracy, reliability, and overall performance.

In one embodiment, the Bayesian framework used involves using knownprior information to establish a framework and then accounting forgathered new information to make inferences from their combination(i.e., combination of prior and new information), to generate posteriorinformation. When the prior information and the new information aremathematically related and combined to form posterior information thatcan be represented functionally, the relationship is defined asconjugate. The Bayesian framework of one embodiment employs conjugatedistributional relationships that result in the posterior distributionalparameters (e.g., mean, variance) being directly available. This can beadvantageous under a computationally restrictive space where both timeand resources are constrained.

In some embodiments, under the Bayesian framework, in which the wedgeparameters follow a prior substantially normal distribution for eachparameter, and in which the algorithmically calculated sensitivity andbaseline values follow their own substantially normal distributionrepresenting the new information, an independent posterior distributionfor sensitivity and/or baseline can be generated. These two posteriordistributions are then used in parallel to construct glucose thresholdvalues used for accepting, rejecting, or modifying a user's manualcalibration entry (e.g., entry from a fingerstick measurement). Incertain embodiments, the posterior distributions of sensitivity andbaseline can also be used for semi-self-calibration, thereby reducingthe number of fingerstick measurements required for manual calibration.

In one exemplary embodiment, from prior information, the wedge min/maxvalues for a sensor's sensitivity to glucose are known to be A and B atthe 95% confidence interval in a substantially normal distribution.Using algorithmically calculated sensitivity values as new information,the previous three, four, five, six, seven, or more sensitivity valuescalculated through manual calibration can be used as new information togenerate a posterior distribution, which will typically have reducedvariability than the distribution based on prior information, and whichis a direct quantification of a unique sensor/user environment. Becauseof reduced variability, the posterior distribution generated willtypically have wedge values, which should be closer together than thedifference between A and B, i.e., the wedge values of the distributiongenerated from prior information. This tightening of the differencebetween the wedge values between the prior distribution and theposterior distribution allow the system to more reliably reject manualcalibration entries that are clearly erroneous, such as, for example, acalibration entry of 60 mg/dL that was intended by the user to be anentry of 160 mg/dL

Bayesian networks use causal knowledge and model probabilisticdependence and independence relationships between different events. FIG.7A depicts distribution curves of sensor sensitivity corresponding tothe Bayesian learning process, in accordance with one embodiment. FIG.7B depicts confidence levels, associated with the sensor sensitivityprofile, that correspond with the distribution curves shown in FIG. 7A.Distribution curve 720 and confidence level 730 (e.g., 25%, 33%, 50%,75%, 95%, or 99% confidence level) are associated with a lack of initialknowledge about certain parameters that affect sensor sensitivity orprovide additional information about sensor sensitivity. For example,distribution curve 720 can be associated with factory information. Asinformation regarding a certain parameter is acquired, the distributioncurve 720′ becomes steeper and the confidence interval 730′ becomesnarrower, as certainty of sensor sensitivity profile 710 is increased.Examples of information that may be used to change the distributioncurves can include a reference analyte value, a cal-check of the sensorat the factory, patient history information, and any other informationdescribed elsewhere herein that can affect sensor sensitivity or provideinformation about sensor sensitivity. As information regarding stillanother parameter is acquired, the distribution curve 720″ becomes evensteeper and the confidence interval 730″ becomes even narrower, ascertainty of sensor sensitivity profile 710 is further increased.

During sensor use, the confidence interval curves 730, 730′, and/or 730″may be used to form the sensitivity profile 710, which provides anestimated sensitivity value at a given time. In turn the estimatedsensitivity value may be used to calibrate the sensor, which allows forprocessing of sensor data to generate a glucose concentration value thatis displayed to the user. In some embodiments, a first estimatedsensitivity profile 710, formed from the confidence interval curves 730,730′, and/or 730″, may be used to monitor and display glucoseconcentrations. In addition, one or more of the confidence intervalcurves 730, 730′, 730″, or combinations thereof, can be used to form asecond estimated sensitivity profile that, while possibly not asaccurate as the first estimated sensitivity profile, is nonetheless morelikely to result in the detection of a hypoglycemic or hyperglycemicrange than the first estimated sensitivity profile 710.

Determination of Baseline

A variety of types of noise can occur when a sensor is implanted in ahost. Some implantable sensors measure a signal (e.g., counts) thatcomprises two components, the baseline signal and the analyte signal.The baseline signal is substantially comprised of a signal contributionfrom factors other than the measure analyte (e.g., interfering species,non-reaction-related hydrogen peroxide, or other electroactive specieswith an oxidation potential that overlaps with the analyte orco-analyte). The analyte signal (e.g., glucose signal) is substantiallycomprised of a signal contribution from the analyte. Consequently,because the signal includes these two components, calibration can beperformed to determine the analyte (e.g., glucose) concentration bysolving for the equation y=mx+b, wherein the value of b represents thebaseline of the signal. In some circumstances, the baseline is comprisedof constant and non-constant non-analyte factors. Generally, it isdesirable to reduce or remove the background signal, to provide a moreaccurate analyte concentration to the patient or health careprofessional.

In certain embodiments, an analyte sensor (e.g., glucose sensor) isconfigured for insertion into a host for measuring an analyte in thehost. The sensor includes a working electrode disposed beneath an activeenzymatic portion of a membrane on the sensor, an auxiliary electrodedisposed beneath an inactive- or non-enzymatic portion of the membraneon the sensor, and sensor electronics operably connected to the workingand auxiliary electrodes. The sensor electronics are configured toprocess signals from the electrodes to generate an analyte (e.g.,glucose) concentration estimate that substantially excludes signalcontribution from non-glucose related noise artifacts.

In some embodiments, the working electrode is configured to generate viasensor electronics a first signal associated with both the analyte andnon-analyte related electroactive compounds that have a oxidationpotential less than or similar to a first oxidation potential. Theauxiliary electrode is configured to generate a second signal associatedwith the non-analyte related electroactive compounds. Non-analyterelated electroactive compounds can be any compound, present in thesensor's local environment, which has an oxidation potential less thanor similar to the oxidation potential of the measured species (e.g.,H2O2). While not wishing to be bound by theory, it is believed that witha glucose-measuring electrode, both the signal directly related to theenzyme-catalyzed reaction of glucose (which produces H2O2 that isoxidized at the first working electrode) and signals from unknowncompounds that are in the extracellular milieu surrounding the sensorcan be measured. These unknown compounds can be constant or non-constant(e.g., intermittent or transient) in concentration and/or effect. Insome circumstances, it is believed that some of these unknown compoundscan be related to the host's disease state. For example, it is knownthat blood chemistry can change dramatically during/after a heart attack(e.g., pH changes, changes in the concentration of various bloodcomponents/protein, and the like). As another example, thetranscutaneous insertion of a needle-type sensor can initiate a cascadeof events that includes the release of various reactive molecules bymacrophages. Other compounds that can contribute to the non-glucoserelated signal are compounds associated with the wound healing process,which can be initiated by implantation/insertion of the sensor into thehost, as described in more detail with reference to U.S. PatentPublication No. US-2007-0027370-A1.

As described above, the auxiliary electrode is configured to generate asecond signal associated with the non-analyte related electroactivecompounds that have an oxidation potential less than or similar to thefirst oxidation potential. Non-analyte related electroactive species caninclude interfering species, non-reaction-related species (e.g., H2O2)that correspond to the measured species, and other electroactivespecies. Interfering species includes any compound that is not directlyrelated to the electrochemical signal generated by the enzyme-catalyzedreaction of the analyte, such as, electroactive species in the localenvironment produced by other bodily processes (e.g., cellularmetabolism, wound healing, a disease process, and the like).Non-reaction-related species includes any compound from sources otherthan the enzyme-catalyzed reaction, such as, H2O2 released by nearbycells during the course of the cells' metabolism, H2O2 produced by otherenzymatic reactions (e.g., extracellular enzymes around the sensor orsuch as can be released during the death of nearby cells or such as canbe released by activated macrophages), and the like. Other electroactivespecies includes any compound that has an oxidation potential less thanor similar to the oxidation potential of H2O2.

The non-analyte signal produced by compounds other than the analyte(e.g., glucose) is considered as background noise and can obscure thesignal related to the analyte, thereby contributing to sensorinaccuracy. As described in greater detail elsewhere herein, backgroundnoise can include both constant and non-constant components and can beremoved to accurately calculate the analyte concentration.

In certain embodiments, the analyte sensor system is configured in a way(e.g., with a certain symmetry, coaxial design, and/or integralformation) such that the working and auxiliary electrodes are influencedby substantially the same external/environmental factors, therebyenabling substantially equivalent measurement of both the constant andnon-constant species/noise. This allows for the substantial eliminationof noise on the sensor signal by using sensor electronics describedelsewhere herein. In turn, the substantial reduction or elimination ofsignal effects associated with noise, including non-constant noise(e.g., transient, unpredictable biologically related noise) increasesaccuracy of continuous sensor signals.

In some embodiments, sensor electronics are operably connected to theworking and auxiliary electrodes. The sensor electronics may beconfigured to measure the current (or voltage) to generate the first andsecond signals. Collectively, the first and second signals can be usedto produce glucose concentration data without substantial signalcontribution from non-glucose-related noise. This can be performed, forexample, by subtraction of the second signal from the first signal toproduce a signal associated with analyte concentration and withoutsubstantial noise contribution, or by alternative data analysistechniques.

In other embodiments, the sensor electronics are operably connected toone or more working electrodes only, as an auxiliary electrode is notneeded. For example, the sensor membrane in some embodiments maycomprise polymers that contain mediators and enzymes that chemicallyattach to the polymers. The mediator used may oxidize at lowerpotentials than hydrogen peroxide, and thus fewer oxidizableinterferents are oxidized at these low potentials. Accordingly, in someembodiments, a very low baseline (i.e., a baseline that approaches azero baseline and that does not receive substantial signal contributionfrom non-glucose-related noise) may be achieved, thereby potentiallyeliminating (or reducing) the need for an auxiliary electrode thatmeasures signal contribution from non-glucose-related noise.

The sensor electronics can be comprised of a potentiostat, A/Dconverter, RAM, ROM, transceiver, processor, and/or the like. Thepotentiostat may be used to provide a bias to the electrodes and toconvert the raw data (e.g., raw counts) collected from the sensor to ananalyte concentration value (e.g., a glucose concentration valueexpressed in units of mg/dL). The transmitter may be used to transmitthe first and second signals to a receiver, where additional dataanalysis and/or calibration of analyte concentration can be processed.In certain embodiments, the sensor electronics may perform additionaloperations, such as, for example, data filtering and noise analysis.

In certain embodiments, the sensor electronics may be configured toanalyze an analyte equivalent baseline or normalized baseline, insteadof the baseline. The normalized baseline is calculated as the b/m ory-intercept divided by slope (from the equation y=mx+b). The unit foranalyte equivalent baseline may be expressed as the analyteconcentration unit (mg/dL), which correlates with the output of thecontinuous analyte sensor. By using the analyte equivalent baseline(normalized baseline), the influence of glucose sensitivity on thebaseline may be eliminated, thereby making it possible to evaluate thebaselines of different sensors (e.g., from the same sensor lot or fromdifferent sensor lots) with different glucose sensitivities.

Although some embodiments have been described herein which employ anauxiliary electrode to allow for the subtraction of a baseline signalfrom a glucose+baseline signal, it should be understood that the use ofthis electrode is optional and may not be used in other embodiments. Forexample, in certain embodiments, the membrane system covering theworking electrode is capable of substantially blocking interferents, andsubstantially reducing the baseline to a level that is negligible, suchthat the baseline can be estimated. Estimation of the baseline can bebased on an assumption of the sensor's baseline at physiologicalconditions associated with a typical patient. For example, baselineestimation can be modeled after in vivo or in vitro measurements ofbaseline in accordance with certain physiological levels of interferentsthat are inherent in the body. FIG. 8 is a graph that provides acomparison between an estimated glucose equivalent baseline and detectedglucose equivalent baseline, in accordance with one study. The estimatedglucose equivalent baseline was formed by conducting in vitromeasurements of the baseline of glucose sensors in a solution thatmimicked physiological levels of interferents in a human. Theinterferents included uric acid with a concentration of about 4 mg/dLand ascorbic acid, with a concentration of about 1 mg/dL of ascorbicacid. As shown in FIG. 8, it was found in this study that the estimatedbaseline closely resembled the detected baseline. Accordingly, with thepossibility of accurate estimation of baseline and/or with a baselinethat is negligible, a single working electrode alone (i.e., without useof an ancillary electrode), together with a predetermined sensorsensitivity profile, may be sufficient to provide a sensor system withself-calibration.

With some embodiments, it has been found that not only does the sensor'ssensitivity tend to drift over time, but that the sensor's baseline alsodrifts over time. Accordingly, in certain embodiments, the conceptsbehind the methods and systems used to predict sensitivity drift canalso be applied to create a model that predicts baseline drift overtime. Although not wishing to be bound by theory, it is believed thatthe total signal received by the sensor electrode is comprised of aglucose signal component, an interference signal component, and aelectrode-related baseline signal component that is a function of theelectrode and that is substantially independent of the environment(e.g., extracellular matrix) surrounding the electrode. As noted above,the term “baseline,” as used herein, refers without limitation to thecomponent of an analyte sensor signal that is not related to the analyteconcentration. Accordingly, the baseline, as the term is defined herein,is comprised of the interference signal component and theelectrode-related baseline signal component. While not wishing to bebound by theory, it is believed that increased membrane permeabilitytypically not only results in an increased rate of glucose diffusionacross the sensor membrane, but also to an increased rate of diffusionof interferents across the sensor membrane. Accordingly, changes insensor membrane permeability over time which causes sensor sensitivitydrift, will similarly also likely cause the interference signalcomponent of the baseline to drift. Simply put, the interference signalcomponent of the baseline is not static, and is typically changing as afunction of time, which, in turn, causes the baseline to also drift overtime. By analyzing how each of the aforementioned components of thebaseline reacts to changing conditions and to time (e.g., as a functionof time, temperature), a predictive model can be developed to predicthow the baseline of a sensor will drift during a sensor session. Bybeing able to prospectively predict both sensitivity and baseline of thesensor, it is believed that a self-calibrating continuous analyte sensorcan be achieved, i.e., a sensor that does not require use of referencemeasurements (e.g., a fingerstick measurement) for calibration.

Calibration Code

The process of manufacturing continuous analyte sensors may sometimes besubjected to a degree of variability between sensor lots. To compensatefor this variability, one or more calibration codes may be assigned toeach sensor or sensor set to define parameters that can affect sensorsensitivity or provide additional information about the sensitivityprofile. The calibration codes can reduce variability in the differentsensors, ensuring that the results obtained from using sensors fromdifferent sensors lots will be generally equal and consistent byapplying an algorithm that adjusts for the differences. In oneembodiment, the analyte sensor system may be configured such that one ormore calibration codes are to be manually entered into the system by auser. In other embodiments, the calibration codes may be part of acalibration encoded label that is adhered to (or inserted into) apackage of multiple sensors. The calibration encoded label itself may beread or interrogated by any of a variety of techniques, including, butnot limited to, optical techniques, RFID (Radio-frequencyidentification), or the like, and combinations thereof. These techniquesfor transferring the code to the sensor system may be more automatic,accurate, and convenient for the patient, and less prone to error, ascompared to manual entry. Manual entry, for instance, possesses theinherent risk of an error caused by a patient or hospital staff enteringthe wrong code, which can lead to an incorrect calibration, and thusinaccurate glucose concentration readings. In turn, this may result in apatient or hospital staff taking an inappropriate action (e.g.,injecting insulin while in a hypoglycemic state).

In some embodiments, calibration codes assigned to a sensor may includea first calibration code associated with a predetermined logarithmicfunction corresponding to a sensitivity profile, a second calibrationcode associated with an initial in vivo sensitivity value, and othercalibration codes, with each code defining parameter that affects sensorsensitivity or provides information about sensor sensitivity. The othercalibration codes may be associated with any priori information orparameter described elsewhere herein and/or any parameter that helpsdefine a mathematical relationship between the measured signal andanalyte concentration.

In some embodiments, the package used to store and transport acontinuous analyte sensor (or sensor set) may include detectorsconfigured to measure certain parameters that may affect sensorsensitivity or provide additional information about sensor sensitivityor other sensor characteristics. For example, in one embodiment, thesensor package may include a temperature detector configured to providecalibration information relating to whether the sensor has been exposedto a temperature state greater than (and or less than) one or morepredetermined temperature values. In some embodiments, the one or morepredetermined temperature value may be greater than about 75° F.,greater than about 80° F., greater than about 85° F., greater than about90° F., greater than about 95° F., greater than about 100° F., greaterthan about 105° F., and/or greater than about 110° F. Additionally oralternatively, the one or more predetermined temperature value may beless than about 75° F., less than about 70° F., less than about 60° F.,less than about 55° F., less than about 40° F., less than about 32° F.,less than about 10° F., and/or less than about 0° F. In certainembodiments, the sensor package may include a humidity exposureindicator configured to provide calibration information relating towhether the sensor has been exposed to humidity levels greater than orless than one or more predetermined humidity values. In someembodiments, the one or more predetermined humidity value may be greaterthan about 60% relative humidity, greater than about 70% relativehumidity, greater than about 80% relative humidity, and/or greater thanabout 90% relative humidity. Alternatively or additionally, the one ormore predetermined humidity value may be less than about 30% relativehumidity, less than about 20% relative humidity, and/or less than about10% relative humidity.

Upon detection of exposure of the sensor to certain levels oftemperature and/or humidity, a corresponding calibration code may bechanged to account for possible effects of this exposure on sensorsensitivity or other sensor characteristics. This calibration codechange may be automatically performed by a control system associatedwith the sensor package. Alternatively, in other embodiments, anindicator (e.g., a color indicator) that is adapted to undergo a change(e.g., a change in color) upon exposure to certain environments may beused. By way of example and not to be limiting, the sensor package mayinclude an indicator that irreversibly changes color from a blue colorto a red color, upon exposure of the package to a temperature greaterthan about 85° F., and also include instructions to the user to enter acertain calibration code when the indicator has a red color. Althoughexposure to temperature and humidity are described herein as examples ofconditions that may be detected by the sensor package, and used toactivate a change in calibration code information, it should beunderstood that other conditions may also be detected and used toactivate a change in calibration code information.

In certain embodiments, the continuous analyte system may comprise alibrary of stored sensor sensitivity functions or calibration functionsassociated with one or more calibration codes. Each sensitivity functionor calibration function results in calibrating the system for adifferent set of conditions. Different conditions during sensor use maybe associated with temperature, body mass index, and any of a variety ofconditions or parameters that may affect sensor sensitivity or provideadditional information about sensor sensitivity. The library can alsoinclude sensitivity profiles or calibrations for different types ofsensors or different sensor lots. For example, a single sensitivityprofile library can include sub-libraries of sensitivity profiles fordifferent sensors made from different sensor lots and/or made withdifferent design configurations (e.g., different design configurationscustomized for patients with different body mass index).

Determining Sensor Properties and Calibrating Sensor Data Using One orMore Stimulus Signals

Some embodiments apply one or more stimulus signals to a sensor todetermine properties of a sensor and/or calibrate sensor data. The term“stimulus signal,” as used herein, is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and are not to be limited to a special or customized meaning), andrefers without limitation to a signal (e.g., any time-varying orspatial-varying quantity, such as an electric voltage, current or fieldstrength) applied to a system being used (e.g., an analyte sensor) tocause or elicit a response.

Non-limiting examples of stimulus signals that can be used in theembodiments described herein can be a waveform including one or more of:a step increase in voltage of a first magnitude, a step decrease involtage of a second magnitude (where the first and second magnitudes canbe the same or different), an increase in voltage over time at firstrate, a gradual decrease in voltage over time having a second rate(where the first rate and the second rate can be different or the same),one or more sine waves overlayed on the input signal having the same ordifferent frequencies and/or amplitudes and the like. A response to thestimulus signal can then be measured and analyzed (the response is alsoreferred to herein as the “signal response”). The analysis can includeone or more of calculating impedance values, capacitance values, andcorrelating the signal response to one or more predeterminedrelationships. As used herein, the term “impendence value” can mean avalue for expressing an electrical impedance, including but not limitedto, a value that only represents a magnitude of impedance or a valuethat express both magnitude and phase of impendence, should impendencebe represented in a polar form, or expresses a real impendence only orboth a real and complex impendence, should impedance be represented in aCartesian form. Based on the calculated impedance values, capacitancevalues and/or predetermined relationships, various sensor properties canbe determined and/or characterized, such as one or more of the sensorproperties discussed herein.

The sensor information can then be used to determine if the analytesensor is functioning properly or not, and/or to calibrate the sensor.For example, the techniques described herein can be used to generatecalibration information (e.g., one or more of baseline, sensorsensitivity, and temperature information) that can in turn be used toform or modify a conversion function, or calibration factor, used toconvert sensor data (e.g., in units of electrical current) into bloodglucose data (e.g., glucose concentration values in units of mg/dL ormmol/L), as described in more detail elsewhere herein. The sensorinformation can alternatively or additionally be used to first correctuncalibrated sensor data (e.g., raw sensor data) and then apply aconversion function to convert the corrected, uncalibrated data tocalibrated sensor data (e.g., glucose concentration values in units ofglucose concentration).

For example, one technique that can be used to determine properties of asystem being used (e.g., an analyte sensor) is Electrochemical ImpedanceSpectroscopy (EIS). EIS is an electrochemical technique based on themeasurement of electrical impedance of the system being used over arange of different frequencies. Changes in the system being used canreflect changes in the frequency spectrum. As an example, a reduction inimpedance may be observed at a particular frequency over a time periodif the system being used has a sensitivity change over that period oftime. Other techniques can also be used to determine properties of asystem being used as discussed further below.

As one illustrative example of how a stimulus signal can be used todetermine sensor properties, reference will now be made to a schematicdiagram of an equivalent sensor circuit model 900 illustrated in FIG. 9.Sensor circuit model 900 can represent electrical properties of ananalyte sensor, such as an embodiment of a continuous glucose sensor.Circuit 900 includes working electrode 904 and reference electrode 902.Operatively connected in serial to reference electrode is Rsolution,representative of a resistance of bulk between the working and referenceelectrodes. The bulk can be a liquid or other medium in which the sensoris placed, such as a buffer solution in the example of a benchlaboratory study, or, in the example of the use as a subcutaneouslyplaced sensor, the bulk can be representative of the resistance of thesubcutaneous tissue between the working and reference electrodes.Operatively connected to Rsolution is Cmembrane, representative of acapacitance of the sensor membrane, and Rmembrane, representative of aresistance of the sensor membrane. A parallel network of Cdouble layerand Rpolarization are operatively connected to Rmembrane. The parallelnetwork of Cdouble layer and Rpolarization is representative of thereactions occurring at the surface of a platinum interface of theworking electrode. In particular, Cdouble layer is representative of thecharge that is built up when a platinum electrode is in the bulk andRpolarization is the polarization resistance of the electrochemicalreactions that occur at the platinum interface.

FIG. 10 is a Bode plot (i.e. |Z_(real) vs. log ω, wherein Z_(real) isreal impedance, ω=2πf and f is frequency) of an analyte sensor inaccordance with one embodiment. The analyte sensor can have propertiesof sensor circuit model 900 of FIG. 9. Referring back to the Bode plotof FIG. 10, the x-axis is the frequency of a stimulus signal applied tothe analyte sensor and the y-axis is the impedance derived from aresponse signal of the analyte sensor. Although not wishing to be boundby theory, it is believed that different frequencies can be used tomeasure or determine different material properties of the sensor. Forexample, in the plot of FIG. 10, the impedance value derived from themeasured response to an input signal having a frequency of about 7 Hzcan be indicative of Cdouble layer, a frequency of about 1 kHz can beindicative of Rmembrane, and a frequency in the range of about 10-20 kHzcan indicative of Cmembrane.

Based on this information, one can determine a state of particularproperties of the sensor by applying a stimulus signal of a particularfrequency or comprising a plurality of frequencies to the sensor anddetermining a sensor impedance based on the signal response. Forinstance, a capacitance of a sensor having the characteristics of theBode plot of FIG. 10 can be determined using a stimulus signal having afrequency in the range of about 1 Hz to 100 Hz, for example 10 Hz orgreater than 10 kHz. In addition, a resistance of a sensor can bedetermined using a stimulus signal having a frequency in the range ofabout 100 Hz to 10 kHz, for example 1 kHz.

FIG. 11 is a flowchart illustrating a process 1100 for determining animpedance of a sensor in accordance with one embodiment. At step 1102, astimulus signal in the form of an active current (ac) voltage at a givenfrequency is applied to a working electrode of the sensor being studied.The ac voltage can be overlayed on a bias potential and can berelatively small as compared to the bias potential, such as a voltagethat is in the range of about 1% to 10% of the bias voltage. In oneembodiment, the ac voltage is a sine wave having an amplitude in therange of 10-50 mV and a frequency in the range of between about 100-1kHz. The sine wave can be overlayed on a 600 mV bias voltage. Theresponse signal (e.g., in units of current) can then be measured in step1104 and analyzed in step 1106 to determine an impedance at the givenfrequency. Should the impedance of the sensor at a range of frequenciesbe of interest, process 1100 can be repeated by applying an ac voltageat each frequency of interest and analyzing a corresponding outputresponse.

Reference will now be made to FIG. 12, which describes a process fordetermining an impedance or plurality of impedances of a sensor beingstudied by applying one or more stimulus signals and converting theresponse signal or signals to a frequency domain in accordance with oneembodiment. The data can be converted to the frequency domain using aFourier transform technique, such as a fast Fourier transform (FFT),discrete time Fourier transform (DTFT) or the like. At step 1202, astimulus signal in the form of a voltage step can be applied to a biasvoltage of the sensor. The voltage step can be in the range of 10-50 mV,for example 10 mV, and the bias voltage can be 600 mV. The signalresponse can then be measured and recorded (e.g., an output current) atstep 1204, and a derivative of the response can be taken at step 1206.At step 1208, a Fourier transform of the derivative of the response canthen be calculated to yield ac currents in the frequency domain. One ormore impedances of the sensor over a wide spectrum of frequencies canthen be calculated based on the ac currents at step 1210.

FIG. 13 is a flowchart of process 1300 for determining an impedance of asensor being studied, such as the impedance of the sensor's membrane, inaccordance with one embodiment. At step 1302, a stimulus signal in theform of a voltage step above a bias voltage is applied to the sensor.The signal response is measured at step 1304, and, at step 1306, a peakcurrent of the response is determined. Next, at step 1308, one or moreimpedance characteristics (e.g., resistance) of the sensor membrane(e.g., Rmembrane) is calculated based on the peak current. The one ormore impedance characteristics can then be correlated to a property ofthe sensor.

In an alternative embodiment, instead of calculating a sensor impedancebased on the peak current, the peak current can be correlated to one ormore predetermined sensor relationships to determine a property of thesensor, such as the sensor's sensitivity. That is, in the alternativeembodiment, the step of calculating the one or more impendencecharacteristics is omitted.

The relationship between a signal response resulting from a stimulussignal in the form of a voltage step and a sensor membrane resistance ofembodiments of analyte sensors will now be discussed further withreference to FIGS. 14A, 14B and FIG. 9.

FIG. 14A is a graph of an input voltage 1400 applied to an analytesensor over time in accordance with one embodiment. The input voltage1400 applied to the analyte sensor initially corresponds to the biasvoltage, which in one embodiment is about 600 mV. A stimulus signal inthe form of a voltage step is then applied to the input voltage at timet1. The magnitude of the voltage step, Δv, can be in the range of 10-50mV, for example 10 mV.

FIG. 14B is a graph of a current response 1402 of the analyte sensor tothe input voltage 1400 of FIG. 14A. As illustrated in FIG. 14B, thecurrent response 1402 can include a sharp spike in current starting attime t2, which corresponds to the time in which the voltage step beginsto impact the response. The current response 1402 includes a peakcurrent at point 1404 and then the current response 1402 graduallydecreases and levels off to a slightly higher level due to the increasein input voltage 1400 as compared to before the voltage step.

In one embodiment, a change in current, Δi, measured as the differencebetween the magnitude of the current response 1402 prior to the voltagestep and the peak current 1404 resulting from the voltage step, can thenbe used to estimate the sensor membrane resistance, such as Rmembrane inFIG. 9. In one embodiment, an estimated sensor membrane resistance canbe calculated in accordance with Ohms Law, where

Rmembrane=Δv/Δi

As discussed above, Δv is the step voltage increase and Δi is the changein current response due to the step voltage increase.

Although not wishing to be bound by theory, it is believed that certainembodiments of sensors provide a direct relationship between a change incurrent in response to a voltage step to the sensor's membraneimpendence characteristics (e.g., resistance).

As a non-limiting example of such a relationship, the followingdescription refers back to sensor circuit model 900 of FIG. 9. In someembodiments, the capacitance, Cmembrane, is much smaller than thecapacitance, Cdouble layer. For example, Cmembrane can have a value thatis about 1/1000 smaller than Cdouble layer. The bulk resistance,Rsolution, of sensor circuit 900 is typically much smaller than theresistance Rmembrane, and the resistance, Rpolarization, can be quitelarge, such as around 3 MOhms. Due to such sensor properties, a voltagestep, Δv, applied to circuit 900 can cause the current to flowsubstantially through circuit 900 along a path from lead 902, throughRsolution, Rmembrane, Cdouble layer and finally to lead 904.Specifically, because capacitive resistance is inversely proportional tothe capacitance and the frequency, the capacitive resistance ofCmembrane is initially very large due to the voltage step, as thevoltage step is, theoretically, an extremely high frequency.Substantially all of the current flows through Rmembrane, rather thanthrough Cmembrane because of the high capacitive resistance ofCmembrane. Further, the current substantially flows through Cdoublelayer instead of Rpolarization because the capacitive resistance ofCdouble is initially small due to the voltage step (high capacitivevalue of Cdouble layer results in low capacitive resistance at highfrequencies, e.g., at the time of a voltage step) and the relativelylarge resistance of Rpolarization. Consequently, the initial totalresistance through which substantially all of the current flows throughcircuit 900 when the step voltage is applied to circuit 900 can besummed up as the series resistance of Rsolution plus Rmembrane. However,because Rsolution is much smaller than Rmembrane in this example, thetotal resistance can be estimated as the membrane resistance, Rmembrane.

Thus, because the membrane resistance, Rmembrane, is essentially theresistance of the circuit 900 at the time of the voltage step, it hasbeen found that the value of Rmembrane can be estimated using Ohms Lawusing the known value of the step increase, Δv, and the measured changein current response, Δi, due to the voltage step.

a. Sensitivity

As discussed herein, a sensor's sensitivity to analyte concentrationduring a sensor session can often change as a function of time. Thischange in sensitivity can manifest itself as an increase in current fora particular level of sensitivity. In some embodiments, the sensitivityincreases during the first 24-48 hours with a relative change in tens ofpercents. In order to provide an accurate analyte concentration readingto a user, system calibrations using reference meters (e.g., strip-basedblood glucose measurements) may be needed. Typically, the rate ofcalibrations can be 1, 2 or more calibrations a day.

As discussed further below, a relationship between sensitivity andimpedance has been observed in embodiments of analyte sensors. Althoughnot wishing to be bound by theory, embodiments of analyte sensors arebelieved to have a relationship between an impedance of a sensor'smembrane and the diffusivity of the membrane. For example, a change inimpedance of an analyte sensor can indicate a proportional change indiffusivity of the analyte sensor's membrane. Further, an increase indiffusivity can yield an increased transport of the analyte beingmeasured (e.g., glucose) through the membrane, resulting in an increasedsensor output current. That is, a change in diffusivity can result in aproportional change in sensor sensitivity. It is noted that otherfactors may also contribute to changes in sensitivity apart from justchanges in diffusivity of the sensor membrane, depending upon thecharacteristics of sensor and the environment in which the sensor isused.

A relationship between sensitivity and impedance can be used to estimatea sensor sensitivity value and/or correct for sensitivity changes of thesensor over time, resulting in increased accuracy, a reduction inrequired calibrations or both. In addition to detection of sensitivity,some embodiments can detect other characteristics of an analyte sensorsystem based on measurements of electrical impedance over one or morefrequencies. These characteristics include, but are not limited to,temperature, moisture ingress into sensor electronics components andsensor membrane damage.

In some exemplary embodiments, a relationship between a sensor'simpedance and the sensor's sensitivity can be used to calculate andcompensate for sensitivity changes of an analyte sensor. For example, achange in impedance of an analyte sensor can correspond to aproportional change in sensitivity of the sensor. In addition, anabsolute value of an impedance of an analyte sensor can correspond to anabsolute value of the analyte sensor's sensitivity and the correspondingsensitivity value can be determined based on a predeterminedrelationship determined from prior studies of similar sensors. Sensordata can then be compensated for changes in sensitivity based on animpedance-to-sensitivity relationship.

FIG. 15 is a flowchart of an exemplary process 1500 for compensatingsensor data for changes in sensitivity in accordance with oneembodiment. At step 1502, a stimulus signal can be applied to the sensorthat can be used to determine an impedance of the sensor's membrane,such as a signal having a given frequency, as discussed with respect toFIG. 11, or a voltage step, as discussed with respect to FIGS. 12-14. Aresponse to the applied signal is then measured at step 1504 and animpedance of the sensor's membrane is determined based on the responseat step 1506. Next, at step 1508, the determined impedance is comparedto an established impedance-to-sensor sensitivity relationship. Theestablished relationship can be determined from prior studies of analytesensors that exhibit similar sensitivity-to-impedance relationships asthe analyte sensor currently being used; for example, sensors that weremade in substantially the same way under substantially the sameconditions as the sensor currently being used. At step 1510, a sensorsignal (e.g., in units of electrical current or counts) of the sensorcurrently being used is corrected using the impedance to sensitivityrelationship. An estimated analyte concentration value or values is thencalculated based on the corrected sensor signal at step 1512 using, forexample, a conversion function. The estimated analyte concentrationvalues can then be used for further processing and/or outputting, suchas triggering alerts, displaying information representative of theestimated values on a user device and/or outputting the information toan external device.

It should be understood that process 1500 is but one example of using animpedance of a sensor to compensate for changes in sensor sensitivity,and that various modifications can be made to process 1500 that fallwithin the scope of the embodiments For example, an establishedimpedance-to-sensitivity relationship can be used to determine asensitivity value of the sensor being used, and the sensitivity valuecan then be used to modify or form a conversion function used to converta sensor signal of the sensor being used into one or more estimatedglucose concentration values. In addition, instead of calculating animpedance based on the stimulus signal response, one or more propertiesof the stimulus signal response (e.g., peak current value, counts, etc.)can be directly correlated to a sensitivity based on a predeterminedrelationship between the stimulus signal property and the sensitivity.

Some embodiments use one or more impedance values of the sensor to form,modify or select a sensitivity profile of an analyte sensor. Asdiscussed above, a sensor can have a sensitivity profile that indicatesthe sensor's change in sensitivity over time. Although sensors made insubstantially the same way under substantially the same conditions canexhibit similar sensitivity profiles, the profiles can still vary. Forexample, the environment in which a particular sensor is used can causethe sensor's sensitivity profile to differ from other, similar sensors.Accordingly, some embodiments can, for example, select a sensitivityprofile out of a plurality of predetermined sensitivity profiles basedon a correlation of the calculated one or more impedance values to theselected sensitivity profile. Further, some embodiments modify a sensorsensitivity profile already associated with the analyte sensor beingused to more closely predict the sensor's sensitivity profile, where themodification is based on the one or more impedance values.

FIG. 16 is a flowchart of an exemplary process 1600 for determining apredicted sensitivity profile using one or more sensor membraneimpedance measurements in accordance with one embodiment. At step 1602,a stimulus signal is applied to an analyte sensor being used and aresponse is measured at step 1604. Next, one or more sensor membraneimpedance values are calculated based on the response at step 1606.Various techniques for calculating sensor membrane impedance valuesbased on the response that can be used in process 1600 are describedelsewhere herein, such as one or more of the techniques discussed withreference to FIGS. 11-14. A sensitivity profile is then determined basedon the one or more calculated impedance values in step 1608. Process1600 then calculates (which can include retrospectively correctingand/or prospectively calculating) estimated analyte concentration valuesusing the determined sensitivity profile. The estimated analyteconcentration values can then be used for further processing andoutputting, such as displaying information representative of theestimated values on a user device and/or outputting the information toan external computing device.

Further to step 1608, various techniques can be used to determine thesensitivity profile. One exemplary technique can compare the one or morecalculated impedance values to a plurality of different predictedsensitivity profiles and select a predicted sensitivity profile thatbest fits the one or more calculated impedance values. The plurality ofdifferent predicted sensitivity profiles can be predetermined and storedin computer memory of sensor electronics, for example. Another techniquethat can be used includes using an estimative algorithm to predict ordetermine a sensitivity profile based on the one or more calculatedimpedance values. A further technique includes determining a sensitivityprofile by modifying a sensitivity profile associated with the sensorbeing used (e.g., a sensor profile previously used to generate estimatedglucose values using the sensor). Modifying the sensitivity profile caninclude using an estimative algorithm to modify the sensitivity profileto more closely track the sensitivity profile of the sensor being usedbased on the one or more calculated impedance values.

Some embodiments compare one or more impedance values of an analytesensor being used to a predetermined or predicted sensitivity profileassociated with the sensor to determine if the sensor is functioningproperly. As discussed above, a sensor can be predicted to have aparticular sensitivity profile based on, for example, a study ofsensitivity changes over time of sensors made in substantially the sameway and used under substantially the same conditions. However, it can bedetermined that a sensor is functioning improperly—due to, for example,improper sensor insertion, damage to the sensor during shipping,manufacturing defects and the like—if the sensor is found not to besufficiently tracking its predicted sensitivity profile based onsensitivities derived from impedance measurements of the sensor. Putanother way, it can be determined that a sensor is not functioningproperly if one or more impedance values of a sensor's membrane do notsufficiently correspond to a predicted sensitivity profile (e.g.,because the impedance of a sensor membrane can indicate a sensitivity ofthe sensor) of the sensor.

FIG. 17 is a flowchart of an exemplary process 1700 for determiningwhether an analyte sensor being used is functioning properly based on apredicted sensitivity profile and one or more impedance measurements. Atstep 1702, a stimulus signal is applied to an analyte sensor being usedand a response is measured at step 1704. Next, one or more sensormembrane impedance values are calculated based on the signal response atstep 1706. Various stimulus signals and techniques for calculatingsensor membrane impedance values based on the signal response that canbe used in process 1700 are described elsewhere herein, such as with anyone of the techniques discussed with reference to FIGS. 11-14. Process1700 then determines a correspondence of the one or more calculatedimpedance values to a sensitivity profile in step 1708. Next, indecision step 1710, process 1700 queries whether the one or morecalculated impedance values sufficiently correspond to the predictedsensitivity profile. If it is determined that the one or more calculatedimpedance values sufficiently correspond to the predicted sensitivityprofile, then process 1700 confirms proper operation of the analytesensor being used. If confirmed to be proper in step 1710, process 1700may then be repeated after a predetermined time delay ranging from about1 minute to 1 day, for example about 10 minutes, 1 hour, 12 hours, or 1day. However, process 1700 initiates an error routine 1712 if it isdetermined that the one or more calculated impedance values do notsufficiently correspond to the predicted sensitivity profile. Errorroutine 1712 can include one or more of triggering and audible alarm,displaying an error message on a user display, discontinuing display ofsensor data on a user display, sending a message to a remotecommunication device over a communication network, such as a mobilephone over a cellular network or remote computer over the internet, andthe like. The error routine can also include modifying the predictedsensitivity profile—based on the one or more impedance measurements, forexample—or selecting a new predicted sensitivity profile based on theone or more impedance measurements. The modified predicted sensitivityprofile or new predicted sensitivity profile can be a sensitivityprofile that more closely corresponds to changes in sensitivity of thesensor being used based on the one or more impedance measurements ascompared to the unmodified or previously used predicted sensitivityprofile.

Further to step 1708 of process 1700, various statistical analysistechniques can be used to determine a correspondence of the one or moreimpedance values to the predicted sensitivity profile. As one example,correspondence can be determined based on whether a sensitivity valuederived from the calculated impedance value (e.g., derived from apredetermined relationship of impedance and sensitivity) differs by noless than a predetermined threshold amount from a predicted sensitivityvalue as determined from the predicted sensitivity profile. Thepredetermined threshold amount can be in terms of an absolute value or apercentage. As another example, correspondence can be determined basedon a data association function. The term “data association function,” asused herein, is a broad term and is used in its ordinary sense,including, without limitation, a statistical analysis of data andparticularly its correlation to, or deviation from, from a particularcurve. A data association function can be used to show data association.For example, sensor sensitivity data derived from impedance measurementsdescribed herein may be analyzed mathematically to determine itscorrelation to, or deviation from, a curve (e.g., line or set of lines)that defines a sensor sensitivity profile; this correlation or deviationis the data association. Examples of a data association function thatcan be used includes, but is not limited to, linear regression,non-linear mapping/regression, rank (e.g., non-parametric) correlation,least mean square fit, mean absolute deviation (MAD), and mean absoluterelative difference. In one such example, the correlation coefficient oflinear regression is indicative of the amount of data association ofsensitivity data derived from impedance measurements from a sensitivityprofile, and thus the quality of the data and/or sensitivity profile. Ofcourse, other statistical analysis methods that determine a correlationof one or more points to a curve can be used in process 1700 in additionto those described herein.

As discussed above, processes 1600 and 1700 can use one or moreimpedance values. When more than one impedance value is used, eachimpedance value can be time-spaced from the other impedance value(s). Inother words, one impedance value can be taken at a first point in timet1 (indicative of a sensor impedance at time t1), a second impedancevalue can be taken at a second, later point in time t2 (indicative of asensor impedance at time t2), and third impedance value taken at athird, even later point in time t3 (indicative of a sensor impedance attime t3), and so on. Further, the time between t1 and t2 can be a firstamount of time and the time between t2 and t3 can be a second amount oftime that is either the same or different than the first amount of time.The time-spaced impedance values can then be used separately or combinedusing a statistical algorithm (e.g., calculating an average or medianvalue of the time-spaced values). The separate values or combined valuecan then be used to determine a sensitivity value and/or sensitivityprofile in step 1608 of process 1600 or determine a correspondence witha sensitivity profile in step 1708 of process 1700, for example.Additionally or alternatively, more than one of the impedance values canbe taken at substantially the same time, but each derived using adifferent measurement technique, such as using two of the measurementtechniques described herein. For example, a first impedance can becalculated using a step voltage technique as described in the process ofFIG. 13, and a second impedance can be calculated using a sine waveoverlay technique as described in the process of FIG. 11. The impedancevalues derived from different measurement techniques can then be appliedto a statistical algorithm (e.g., calculating an average or medianvalue) to determine a processed impedance value. The processed impedancevalue can then be used to determine a sensitivity value and/orsensitivity profile in step 1608 of process 1600 or determine acorrespondence with a sensitivity profile in step 1708 of process 1700,for example.

b. Temperature

Some embodiments can use signal processing techniques to determine atemperature of the sensor. For example, a stimulus signal can be appliedto a sensor and a signal response measured and, based on the signalresponse, a temperature of the sensor can be derived.

An impedance of a sensor membrane, as determined using one of thetechniques described with reference to FIGS. 11-14, for example, can beused to estimate a temperature of the sensor in accordance with oneembodiment. Although not wishing to be bound by theory, it is believedthat sensitivity of a sensor is affected by temperature, where a highertemperature can result in a higher sensitivity and a lower temperaturecan result in a lower sensitivity. Similarly, because an impedance of asensor membrane can have a direct relationship to the sensor'ssensitivity, it is believed that a higher temperature can result inlower impedance and a lower temperature can result in higher impedance.That is, sensitivity and impedance can have a direct relationship to thesensor's temperature. Accordingly, using a known relationship betweenimpedance and temperature—based on previously conducted studies ofsubstantially similar sensors, for example—one can estimate a sensor'stemperature based on a sensor impedance measurement.

FIG. 18 is a flowchart of an exemplary process 1800 for determining asensor temperature in accordance with one embodiment. At step 1802, astimulus signal is applied to an analyte sensor being used, and aresponse is measured and recorded at step 1804. Impedance is calculatedbased on the signal response at step 1806. The impedance can becalculated using, for example, any of the techniques described hereinsuch as those described with reference to FIGS. 11-14. A temperature ofthe sensor is then estimated based on a predetermined relationshipbetween impedance and temperature at step 1808. The temperature can thenbe used to estimate analyte concentration values (e.g., glucoseconcentration) using sensor data or otherwise used for furtherprocessing and/or outputting. For example, the temperature can be usedto compensate for temperature effects on sensor sensitivity, moreaccurate analyte concentration values can be estimated based on thesensitivity compensation, and the more accurate analyte concentrationscan be outputted to a display or used to trigger an alert using aglucose monitoring system.

A relationship between sensor sensitivity and different temperatures canbe mathematically modeled (e.g., by fitting a mathematical curve to datausing one of the modeling techniques described herein), and themathematical model can then be used to compensate for temperatureeffects on the sensor sensitivity. That is, a sensitivity of a sensor(which is affected by the sensor's temperature) can be determined basedon associating a measured impedance of the sensor to the mathematicalcurve. The predetermined relationship between impedance and temperaturecan be determined by studying impedances of similar sensors over a rangeof temperatures. Sensor data can then be converted to estimated analyteconcentration values based on the determined sensor sensitivity.

As a non-limiting example, some embodiments of analyte sensors can havean essentially linear relationship of impedance to temperature after asensor run-in period (e.g., a period of time after sensor implantationin which the sensor stabilizes, which can last one to five hours in someembodiments). The slope of the linear relationship can be established bystudying sensors made in substantially the same way as the sensor beingused over a range of temperatures. Thus, a sensor temperature can beestimated by measuring an impedance value of the sensor's membrane andapplying the impedance value to the established linear relationship.Other embodiments can have a non-linear relationship of impedance totemperature and with these other embodiments the relationship can becharacterized using an established non-linear relationship.

Some embodiments can compare a first sensor temperature, where the firsttemperature is derived from an impedance measurement of an analytesensor, with a second sensor temperature, where the second sensortemperature is derived independent from the impedance measurement. Thesecond estimated temperature can be measured using a thermistor, forexample. In the example of using a thermistor, the thermistor can beconfigured to measure an in vivo or ex vivo temperature, and can belocated on the analyte sensor or separate from the analyte sensor. Asnon-limiting examples, the thermistor can be integral with the analytesensor, positioned on the surface of the skin of a host adjacent to aninsertion site in which the analyte sensor is implanted, positioned onthe skin of the host at a location away from the insertion site orspaced apart from the host entirely, such as on a handheld devicecarried by the host. Factors contributing to a change in sensorsensitivity or a change in other sensor properties can then bedetermined or confirmed based, at least in part, on the comparison ofthe first and second temperatures.

c. Moisture Ingress

In some embodiments, moisture ingress into sensor electronics can bedetermined based on measuring an impedance of the sensor at a particularfrequency or range of frequencies. Should the measured impedance notcorrespond sufficiently with predetermined impedance value(s), then asensor system operatively connected to the sensor can initiate amoisture ingress error routine. Correspondence can be determined usingone or more threshold, a data association function, and the like.Further, it should be noted that it has been found that impedance phaseinformation can provide beneficial information in determining moistureegress. Accordingly, in some embodiments, the impedance measurement canbe broken into separate impendence magnitude and phase components, andone or both impendence components can be compared to predeterminedvalues to determine the correspondence.

FIG. 19 is a flowchart of an exemplary process 1900 for determiningmoisture ingress. At step 1902, a stimulus signal having a particularfrequency or a signal comprising a spectrum of frequencies (e.g., avoltage step) is applied to an analyte sensor being used, and a signalresponse is measured and recorded at step 1904. Impedance magnitude andphase is calculated based on the signal response at step 1906. Process1900 then determines whether the impedance magnitude and phase valuesfall within respective predefined levels at decision step 1908. If theimpedance magnitude and phase values exceed one or both of therespective predefined levels, then process 1900 initiates an errorroutine at step 1910. The error routine can include one or more oftriggering an audible alarm and/or visual alarm on a display screen toalert a user that the sensor system may not be functioning properly. Thealarm can notify a user that the current sensor system is defective, forexample. If, on the other hand, one or both of the impedance and phasevalues fall within the respective predefined levels, then process 1900ends.

Although the above description describes calculating separate impedanceand phase values, it is understood that the above process 1900 candetermine a complex impedance value and determine a correspondence ofthe determined complex impedance value to one or more predeterminedcomplex impedance value(s), such as by using a data association functionor comparing the determined complex impedance to a threshold or range ofpredetermined complex impendence values. The error routine can then beinitiated responsive to the correspondence.

d. Membrane Damage

In some embodiments, membrane damage can be detected based on measuringan impedance at a particular frequency or range of frequencies. Shouldthe measured impedance not correspond sufficiently with predeterminedone or more impedance values, then a sensor system operatively connectedto the sensor can initiate a membrane damage error routine.Correspondence can be determined using a data association function.

FIG. 20 is a flowchart of an exemplary process 2000 for determiningmembrane damage. At step 2002, a stimulus signal having a particularfrequency, multiple signals having different frequencies and/or a signalcomprising a spectrum of frequencies is applied to an analyte sensorbeing used, and the signal response(s) is/are measured and recorded atstep 2004. Both impedance magnitude and phase is calculated based on thesignal response(s) at step 2006. Process 2000 then determines whetherthe impedance magnitude and phase value(s) fall within respectivepredefined levels at decision step 2008. If the impedance magnitude andphase values exceed one or both of the respective predefined levels,then process 2000 initiates an error routine at step 2010. The errorroutine can include one or more of triggering an audible alarm and/orvisual alarm on a display screen to alert a user that the sensor systemis not functioning properly. The alarm can notify a user that thecurrently used sensor is damaged and needs to be replaced, for example.If, on the other hand, one or both of the impedance magnitude and phasevalues fall within the respective predefined levels, then process 2000ends.

Although the above description describes using separate impedancemagnitude and phase values, it is understood that the above process 2000can use a complex impedance value and determine a correspondence of thecomplex impedance value to a predetermined values or levels. The errorroutine can be initiated responsive to the determined correspondence.

e. Sensor Reuse

In some embodiments, sensor reuse can be detected. Embodiments ofglucose sensors described herein may have a defined life in which asensor can provide reliable sensor data. After the defined life, thesensor may no longer be reliable, providing inaccurate sensor data. Toprevent use beyond the predefined life, some embodiments notify a userto change the sensor after it has been determined that the sensor shouldno longer be used. Various methods can be used to determine whether asensor should no longer be used, such as a predetermined amount of timetranspiring since the sensor was first used (e.g., when first implantedinto a user or when first electrically connected to a sensor electronicsmodule) or a determination that the sensor is defective (e.g., due tomembrane rupture, unstable sensitivity and the like). Once it isdetermined that the sensor should no longer be used, the sensor systemcan notify a user that a new sensor should be used by audibly and/orvisually prompting a user to use a new sensor and/or shutting down adisplay or ceasing to display sensor data on the display, for example.However, a user may try to reuse the same sensor instead of using a newsensor. This can be dangerous to the user because the sensor may provideinaccurate data upon which the user may rely.

Accordingly, some embodiments can be configured determine sensor reusebased at least in part on one or more measurements of the impedance ofthe sensor. As discussed in more detail elsewhere herein, the impedancethat relates to the membrane resistance of a sensor is typicallyinitially high and then gradually decreases as the sensor is run-in.Thus, in one embodiment, sensor re-use can be detected if an impedancemeasured soon after sensor implantation is greater than what a sensorshould typically have when a sensor is initially implanted, as this canindicate that the sensor had already been used.

FIG. 21 is a flowchart of an exemplary process 2100 for determiningsensor reuse in accordance with one embodiment. At step 2102, a sensorinsertion event is triggered. An insertion event can be one of anynumber of possible events that indicate a new sensor has been implanted,such as a user providing input to a sensor system that a new sensor hasbeen implanted, the sensor system detecting electrical connection to asensor, a predetermined amount of time transpiring since the systemprompted a user to use a new sensor, and the like. Next, at step 2104, astimulus signal is applied to an analyte sensor being used, and aresponse is measured and recorded at step 2106. Impedance is calculatedbased on the signal response at step 2108. The stimulus signal andtechnique for calculating impedance in steps 2106 and 2108 can be any ofthe signals and techniques described herein such as those described withreference to FIGS. 11-14. Then, at decision step 2110, the calculatedimpedance is compared to a predetermined threshold. Should it bedetermined that the impedance exceeds the threshold, then a sensor reuseroutine is initiated at step 2112. If it is determined in decision step2110 that the impedance does not exceed the threshold, then the process2100 ends at step 2114.

The sensor reuse routine of step 2112 can include triggering an audibleand/or visual alarm notifying the user of improper sensor reuse. Thealarm can also inform the user why sensor reuse may be undesirable, suchas potentially providing inaccurate and unreliable sensor readings. Thesensor reuse routine 2112 can alternatively or additionally cause thesensor system to fully or partially shut down and/or cease display ofsensor data on a user interface of the sensor system.

In an embodiment, recent impedance measurement information (e.g., one ormore recent impedance measurement values) of a previously-used sensor(such as the sensor used immediately prior to the newly implantedsensor) or a predetermined sensor profile can be stored in computermemory and compared to impedance measurement information of a newlyimplanted sensor (e.g., what is supposed to be a newly used sensor). Itcan then be determined that the sensor is being reused should thecomparison indicate that the impedances of the previously used sensor ata time close to when the use of the prior sensor was discontinued (e.g.,removed) and the newly inserted sensor are too similar using, forexample, a data association function, as a new sensor should have asignificantly different impedance soon after initial implantation thanthe impedance of the previously used sensor substantially prior to itsdiscontinuation of use. If it is determined that the sensor is beingreused, then the sensor system can trigger an error routine, which caninclude notifying the user via audible and/or visual alarms using a userinterface of the sensor system of improper sensor reuse and promptingthe user to use a new sensor, as discussed above with respect to step2112.

FIG. 22 is a flowchart of another exemplary process 2200 for determiningsensor reuse in accordance with one embodiment. At step 2202, a sensorinsertion event is triggered. A an insertion event can be one of anynumber of possible events that indicate a new sensor has been implanted,such as a user providing input to a sensor system that a new sensor hasbeen implanted, the sensor system detecting electrical connection to asensor, a predetermined amount of time transpiring since the systemprompted a user to use a new sensor, and the like. Next, at step 2204, astimulus signal is applied to an analyte sensor being used, and aresponse is measured and recorded at step 2206. Impedance is calculatedbased on the signal response at step 2208. The stimulus signal andtechnique for calculating impedance in steps 2206 and 2208 can be any ofthe signals and techniques described herein such as those described withreference to FIGS. 11-14. Then, at decision step 2210, the calculatedimpedance is compared to one or more previously measured impedancevalues measured using one or more previously implanted sensors (at whatat least should have been a previously implanted sensor according to thesensor system). Should it be determined that the calculated impedancecorrelates within a predetermined amount with the one or more previouslymeasured impedance measurements, then a sensor reuse routine isinitiated at step 2112. Correlation can be determined using a dataassociation function, such as one of the data association functionsdescribed herein. If it is determined in decision step 2110 that theimpedance does not correlate within the predetermined amount topreviously measured impedance values, then the process 2100 ends at step2114, wherein the system continues to use the sensor to measure glucoseconcentrations of the host.

The sensor reuse routine of step 2212 can include triggering an audibleand/or visual alarm notifying the user of improper sensor reuse. Thealarm can also inform the user why sensor reuse may be undesirable, suchas potentially providing inaccurate and unreliable sensor readings. Thesensor reuse routine 2212 can alternatively or additionally cause thesensor system to fully or partially shut down and/or cease display ofsensor data on a display of the sensor system.

f. Sensor Overpotential

Some embodiments apply an overpotential routine based on one or moremeasured impedances of the sensor membrane. It has been found thatapplying an overpotential (e.g., a voltage potential greater than thebias potential applied to the sensor when used for continuous analytesensing) to some embodiments of analyte sensors can aid in stabilizingthe analyte sensor, thereby reducing a run-in period of the sensor. Theoverpotential may need to be discontinued once the sensor hassufficiently stabilized, otherwise damage to the sensor can occur.Accordingly, one or more impedance measurements of the sensor can beused to determine a sensitivity or change in sensitivity of the sensor.Any of the techniques described herein, such as those described withreference to FIGS. 11-14, can be used to measure an impendence of thesensor. The determined sensitivity or sensitivity change can be, inturn, used to indicate whether or not the sensor has stabilized or willstabilize within a determined amount of time by, for example,determining a correspondence of the measured impedance to predeterminedsensitivity-to-impedance relationships. Upon determining that the sensorhas stabilized or will stabilize within a determined amount of time,application of the overpotential can be discontinued or reduced. Inaddition, a magnitude of an overpotential and/or length of time in whichthe overpotential is to be applied to the sensor can be determined ormodified based on one or more impedance measurements taken prior toapplication of the overpotential or during application of theoverpotential. That is, an overpotential routine can be modified ordiscontinued according to one or more impedance measurements inaccordance with some embodiments.

g. Multi-Electrode or Multi-Sensor Configuration

Some embodiments of sensor systems comprise a plurality of sensorelectrodes. For example, as discussed above, in addition to an analytesensing electrode, some embodiments can include an auxiliary electrodeto allow for the subtraction of a baseline signal from ananalyte+baseline signal. Some embodiments can also include one or moreredundant analyte sensors.

In accordance with one embodiment, a first stimulus signal can beapplied to a first sensor and a second stimulus signal can be applied toa second sensor. The first sensor can be configured to sense an analyteconcentration (e.g., glucose) and, in this regard, can generate ananalyte+baseline signal. The second sensor can be an auxiliary sensorconfigured to measure a baseline signal that can be subtracted from thesignal of the first sensor. In addition, the first stimulus signal canhave the same waveform or different waveform than the second stimulussignal. A response to the first stimulus signal and a response to thesecond stimulus signal can each be measured and recorded. The firstresponse and the second response can be processed, and sensorcharacteristics can be determined based on the processing, such asimpedance values associated with each sensor. The processing can includea comparison (e.g., using a data association function described herein)of the first and second response signals to one another and/or compareeach of the first and second response signals to establishedrelationships. The sensor characteristics can include any of the sensorcharacteristics described herein, including a sensitivity value orsensitivity change of the first and/or second electrodes, a temperatureof the first and/or second electrodes, magnitude of detected membranedamage of the first electrode, and the like. Further, sensor data can becompensated for changes in sensor characteristics, such as changes insensor sensitivity, based on the processing of the first and secondresponse signals.

FIG. 23 is a schematic diagram of a dual-sensor configuration that canbe used in a sensor system in accordance with some embodiments. Thedual-sensor configuration includes a first sensor 2302, having a workingelectrode 2304, reference electrode 2306 and membrane 2308, and a secondsensor 2310, also having a working electrode 2312, reference electrode2314 and membrane 2316. Each sensor can be essentially the same,including having the same type of membrane, or each sensor can bedifferent, such as having different membranes or even one of the sensorsnot having a membrane. In one embodiment, each sensor 2302 and 2310 isconfigured to measure an analyte concentration in a host. In analternative embodiment, the second sensor 2310 does not have a discretereference electrode. In this alternative embodiment, the referenceelectrode of the first sensor can also function as the referenceelectrode of the second sensor.

As illustrated in FIG. 23, a stimulus signal can be applied to the firstsensor 2302 using signal generator/detector 2318 (the stimulus signalcan be any stimulus signal described herein, such as a voltage step).The stimulus signal can elicit electric field lines 2320 to emanate fromthe first sensor 2302 and evoke an electrical response in the secondsensor 2310. The electrical response can be measured using signalgenerator/detector 2322 electrically connected to the second sensor2314. The signal generator/detectors 2318 and 2322 can include any knownelectrical circuitry capable of generating a desired stimulus signaldiscussed herein and capable of measuring a response to the stimulussignal.

Further to FIG. 23, the response measured in the second sensor 2310 canthen be used to determine sensor properties, such as any sensorproperties discussed herein. For example, the response can be used tocalculate an impedance of the first sensor 2302 and thereafter used todetermine a sensitivity of the first sensor 2302 and/or correct sensordata generated by the first sensor, using one of the processes describedherein.

Alternatively or in addition, a first stimulus signal can be applied tothe first sensor 2302 and measured using the second sensor 2310 and asecond stimulus signal can be applied to the second sensor 2310 andmeasured using the first sensor 2302. The first and second stimulussignals can be essentially the same or can be different. The responsesto each of the first and second stimulus signals can then be used todetermine a sensor property, such as any of the sensor propertiesdiscussed herein.

The sensor to which the stimulus signal is applied can also be used tomeasure a response to the stimulus signal in addition to the othersensor measuring a response to the stimulus signal. For example, a firststimulus signal can be applied to the first sensor 2302 and first andsecond responses to the first stimulus signal can be measured by thefirst sensor 2302 and the second sensor 2310, respectively. The firstand second responses can then be used to determine sensor propertiesdiscussed herein of either or both of the first sensor 2302 and secondsensor 2310.

h. Scaling Factor

In some embodiments, a scaling factor can be used to correct differencesin responses of a dual-electrode analyte sensor. In some embodiments, adual-electrode analyte sensor that can be used is a referencesensor/system, whereby reference data can be provided for calibration(e.g., internal to the system), without the use of an external (e.g.,separate from the system) analyte-measuring device. In some embodiments,the dual-electrode analyte sensor includes a first electrode thatincludes an enzyme reactive with a particular analyte (which can bereferred to as the plus-enzyme electrode) and a second electrode thatdoes not include the enzyme (which can be referred to as theminus-enzyme electrode).

In some embodiments, the sensor system (such as a sensor electronicsmodule of the sensor system) is configured to determine a scaling factor(k). Briefly, a scaling factor defines a relationship between theelectrodes of the dual-electrode analyte sensor. Accordingly, in someembodiments, the sensor system is configured to calibrate the analytesensor data using the scaling factor, such that the calibrated sensordata does not include inaccuracies that can arise due to differencesbetween the first and second working electrodes, respectively. That is,the scaling factor can be used to calculate estimated analyte valuesbased on data generated by the sensor system

U.S. Patent Publication No. US-2009-0242399-A1, the contents of whichare hereby incorporated by reference in its entirety, describes in moredetail dual-electrode analyte sensor systems, scaling factors andmethods for using scaling factors that can be used in some embodiments.

In accordance with some embodiments, the membrane impedance of eachelectrode of a dual-electrode system can be used to determine or updatea scaling factor. The scaling factor can then be used to calculateestimated analyte concentration values based on data generated by thesensor system.

The following illustrates an exemplary process for determining a scalingfactor using impendence in accordance with one embodiment. First,membrane impedance is measured for both electrodes of a dual-electrodesensor system. Techniques described herein, such as those described withreference to FIGS. 11-14 and under the heading “Multi-electrode ormulti-sensor configuration” can be used to measure the membraneimpendence of each of the electrodes of the dual-electrode analytesensor. The impendence can be measured periodically during sensor use. Ascaling factor can be generated using a ratio of the measured membraneimpendence of the two electrodes (e.g., a ratio of the membraneimpendence of a plus-enzyme electrode and the membrane impedance of aminus-enzyme electrode). A scaling factor to generate estimated analytevalues can then be determined or updated based on the generated scalingfactor. The determined or updated scaling factor can then be used togenerate estimated analyte values.

It has been found that acetaminophen can interfere with glucosemeasurements using some sensor embodiments. It has also been found thatacetaminophen response can be proportional to its diffusion through asensor membrane and sensor impedance can be indicated of membraneimpedance. Thus, the above illustrative process for determining ascaling factor can be used to periodically determine an acetaminophenscaling factor by determining a ratio of the plus-enzyme andminus-enzyme electrode impedance. The acetaminophen scaling factor canbe used to update a scale factor, such as one of those described above,used to calculate estimated glucose concentrations.

i. Calibration

An exemplary calibration process in accordance with some embodimentswill now be discussed with reference to FIG. 24. Calibration process2400 can use one or more of pre-implant information 2402, internaldiagnostic information 2404 and external reference information 2406 asinputs to form or modify a conversion function 2408. Conversion function2408 can be used to convert sensor data (e.g., in units of current orcounts) into estimated analyte values 2410 (e.g., in units of analyteconcentration). Information representative of the estimated analytevalues can then outputted 2412, such as displayed on a user display,transmitted to an external device (e.g., an insulin pump, PC computer,mobile computing device, etc.) and/or otherwise processed further. Theanalyte can be, glucose, for example.

In process 2400, pre-implant information 2402 can mean information thatwas generated prior to implantation of the sensor(s) presently beingcalibrated. Pre-implant information 2402 can include any of thefollowing types of information:

-   -   predetermined sensitivity profile(s) associated with the        currently used (e.g., implanted) sensor, such a predicted        profile of sensitivity change over time of a sensor;    -   previously determined relationships between particular stimulus        signal output (e.g., output indicative of an impedance,        capacitance or other electrical or chemical property of the        sensor) to sensor sensitivity (e.g., determined from prior in        vivo and/or ex vivo studies);    -   previously determined relationships between particular stimulus        signal output (e.g., output indicative of an impedance,        capacitance or other electrical or chemical property of the        sensor) to sensor temperature (e.g., determined from prior in        vivo and/or ex vivo studies);    -   sensor data obtained from previously implanted analyte        concentration sensors;    -   calibration code(s) associated with a sensor being calibrated,        as discussed herein;    -   patient specific relationships between sensor and sensitivity,        baseline, drift, impedance, impedance/temperature relationship        (e.g., determined from prior studies of the patient or other        patients having common characteristics with the patient);    -   site of sensor implantation (abdomen, arm, etc.) specific        relationships (different sites may have different vascular        density);    -   time since sensor manufacture (e.g., time sensor on shelf, date        when sensor was manufactured and or shipped, time between when        the sensor was manufactured an/or shipped and when the sensor is        implanted); and    -   exposure of sensor to temperature, humidity, external factors,        on shelf.

In process 2400, internal diagnostic information 2402 can meaninformation generated by the sensor system in which the implantedanalyte sensor (the data of which is being calibrated) is being used.Internal diagnostic information 2402 can include any of the followingtypes of information:

-   -   stimulus signal output (e.g., the output of which can be        indicative of the sensor's impedance) of sensor using any of the        stimulus signal techniques described herein (the stimulus signal        output can be obtained and processed in real time);    -   sensor data measured by the implanted sensor indicative of an        analyte concentration (real-time data and/or previously        generated sensor data using the currently implanted sensor);    -   temperature measurements using the implanted sensor or an        auxiliary sensor (such as a thermistor) co-located with the        implanted analyte sensor or separately from the implanted        analyte sensor;    -   sensor data from multi-electrode sensors; for example, where one        electrode of the sensor is designed to determine a baseline        signal as described herein;    -   sensor data generated by redundant sensors, where one or more of        the redundant sensors is designed to be substantially the same        as at least some (e.g., have the same sensor membrane type), if        not all, of the other redundant sensors;    -   sensor data generated by one or more auxiliary sensors, where        the auxiliary sensor is having a different modality such (as        optical, thermal, capacitive, etc.) co-located with analyte        sensor or located apart from the analyte sensor;    -   time since sensor was implanted and/or connected (e.g.,        physically or electronically) to a sensor electronics of a        sensor system;    -   data representative of a pressure on sensor/sensor system        generated by, for example, a pressure sensor (e.g., to detect        compression artifact);    -   data generated by an accelerometer (e.g., indicative of        exercise/movement/activity of a host);    -   measure of moisture ingress (e.g., indicative of an integrity of        a moisture seal of the sensor system); and    -   a measure of noise in an analyte concentration signal (which can        be referred to as a residual between raw and filtered signals in        some embodiments).

In process 2400, external reference information 2402 can meaninformation generated from sources while the implanted analyte sensor(the data of which is being calibrated) is being used. Externalreference information 2402 can include any of the following types ofinformation:

-   -   real-time and/or prior analyte concentration information        obtained from a reference monitor (e.g., an analyte        concentration value obtained from separate sensor, such as a        finger stick glucose meter);    -   type/brand of reference meter (different meters can have        different bias/precision);    -   information indicative of carbohydrates consumed by patient;    -   information from a medicament pen/pump, such as insulin on        board, insulin sensitivity, glucagon on board;    -   glucagon sensitivity information; and    -   information gathered from population based data (e.g., based on        data collected from sensors having similar characteristics, such        as sensors from the same lot).

Exemplary Sensor System Configurations

Embodiments of the present invention are described above and below withreference to flowchart illustrations of methods, apparatus, and computerprogram products. It will be understood that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, can be implemented by execution of computer programinstructions. These computer program instructions may be loaded onto acomputer or other programmable data processing apparatus (such as acontroller, microcontroller, microprocessor or the like) in a sensorelectronics system to produce a machine, such that the instructionswhich execute on the computer or other programmable data processingapparatus create instructions for implementing the functions specifiedin the flowchart block or blocks. These computer program instructionsmay also be stored in a computer-readable memory that can direct acomputer or other programmable data processing apparatus to function ina particular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture includinginstructions which implement the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart block or blocks presented herein.

In some embodiments, a sensor system is provided for continuousmeasurement of an analyte (e.g., glucose) in a host that includes: acontinuous analyte sensor configured to continuously measure aconcentration of the analyte in the host and a sensor electronics modulephysically connected to the continuous analyte sensor during sensor use.In one embodiment, the sensor electronics module includes electronicsconfigured to process a data stream associated with an analyteconcentration measured by the continuous analyte sensor in order toprocess the sensor data and generate displayable sensor information thatincludes raw sensor data, transformed sensor data, and/or any othersensor data, for example. The sensor electronics module can includeelectronics configured to process a data stream associated with ananalyte concentration measured by the continuous analyte sensor in orderto process the sensor data and generate displayable sensor informationthat includes raw sensor data, transformed sensor data, and/or any othersensor data, for example. The sensor electronics module can include aprocessor and computer program instructions to implement the processesdiscussed herein, including the functions specified in the flowchartblock or blocks presented herein.

In some embodiments, a receiver, which can also be referred to as adisplay device, is in communication with the sensor electronics module(e.g., via wired or wireless communication). The receiver can be anapplication-specific hand-held device, or a general purpose device, suchas a P.C., smart phone, tablet computer, and the like. In oneembodiment, a receiver can be in data communication with the sensorelectronics module for receiving sensor data, such as raw and/ordisplayable data, and include a processing module for processing and/ordisplay the received sensor data. The receiver can also and include aninput module configured to receive input, such as calibration codes,reference analyte values, and any other information discussed herein,from a user via a keyboard or touch-sensitive display screen, forexample, and can also be configured to receive information from externaldevices, such as insulin pumps and reference meters, via wired orwireless data communication. The input can be processed alone or incombination with information received from the sensor electronicsmodule. The receiver's processing module can include a processor andcomputer program instructions to implement any of the processesdiscussed herein, including the functions specified in the flowchartblock or blocks presented herein.

EXAMPLES

Embodiments are further detailed in the following Examples, which areoffered by way of illustration and are not intended to limit theinvention in any manner. As disclosed herein, studies have beenconducted using a Gamry potentiostat system to analyze complex impedanceof glucose sensors placed in a buffer. The Gamry potentiostat system iscommercially available from Gamry under the model name Ref600. Thebuffer is a modified isolyte having a known concentration of glucose. Inmany of the examples, the glucose concentration is about 100 mg/dL. Theimpedance measurement error in the examples was found to be about 1 to5% by testing the system with a known impedance.

Although some of the following examples were performed on a lab bench,the sensors under test are configured to be used in vivo to continuouslyor substantially continuously measure a glucose concentration of a host.

Analyte sensors used in the following examples were selected fromdifferent types of sensors. The sensors under test include sensors takenfrom different sensor lots, where sensors from a first lot may have beenmade in a different way and under different conditions, which can resultin sensors for different lots exhibiting different sensitivity profiles.Further, some of the sensors studied in these examples are configuredfor placement in transcutaneous tissue of a host to measure the host'sglucose concentration, while other sensors are configured to measure anintravenous blood glucose concentration of a host. In the followingexperiments, sensors intended to be used to measure an intravenous bloodglucose concentration can be referred to as an “IVBG sensor type” andsensors intended to measure a blood glucose concentration intranscutaneous tissue of a host can be referred to as a “transcutaneoussensor type.”

Example 1

Sensitivity and Impedance Relationship

Example 1 illustrates a relationship between sensitivity of a sensor andan impedance of the sensor. In this example, an IVBG sensor wasconnected to a Gamry potentiostat system and placed in a in a buffersolution of a modified isolyte having a glucose concentration of 100mg/dL. The temperature during the experiment was 37 C. An impedancespectrum was captured at fixed intervals of time. The impedance spectrumanalyzed in this experiment ranged from 1 Hz to 100 kHz and measurementsof the sensor impedance and sensor sensitivity were taken at 15 minuteintervals over a period of about 1200 minutes.

Reference is now made to FIG. 25, which is a graph showing absolutevalues of sensitivity and impedance of the sensor based on an inputsignal having a frequency of 1 kHz. Data points 2502 represent measuredvalues of sensor sensitivity over a time period of 1200 minutes (20hours), where t=0 corresponds to the time when the sensor is initiallyplaced in the buffer. Data points 1104 represent measured values ofimpedance over the same time period.

The sensitivity and impedance values of FIG. 25 appear to have aninverse correlation. That is, the sensitivity initially increasesquickly and then the rate of the increase gradually slows down andlevels off, and the impedance initially deceases quickly and then therate of the decrease gradually slows down and levels off. Although notwishing to be bound by theory, the initial increase in sensitivity anddecrease in impedance is believed to be due to sensor run-in.

FIG. 26 is a plot of the data in FIG. 25, but the sensitivity andimpedance are in terms of percent change versus one hour instead ofabsolute values. As can be seen, the sensitivity 2602 and impedance 2604relative changes versus one hour also appear to have an inversecorrelation.

Example 2 Retrospectively Compensating for Sensitivity Drift UsingImpedance

FIG. 27 is a plot of sensitivity and impedance points measured atvarious intervals over time using seven different sensors, Sensors A-G.Sensors A-G were transcutaneous-type sensors, but selected from severaldifferent sensor lots. Thus, even though Sensors A-G were alltranscutaneous sensors, sensors from different lots may have been madein a slightly different way or under slightly different conditions,which can result in sensors from different lots exhibiting differentsensitivity profiles. In this Example, Sensors A and D were selectedfrom a first lot, Sensor B was selected from a second lot, and SensorsC, E, F and G were selected from a third lot.

Further to FIG. 27, the plotted data points are sensitivity andimpedance values for each Sensor A-G. Because the sensitivity of eachSensor A-G gradually increases over time, the right most points of eachSensor's plotted data points tend to correspond to values measuredaround t=0 and the left most points tend to correspond to values ataround t=24 hr.

As can be seen in FIG. 27, the impedance and sensitivity values of eachSensor A-G have an essentially linear relationship, where thesensitivity gradually increases and the impedance decreasescorrespondingly over time. The data taken from all seven Sensors A-Ggenerally follow this linear relationship, although the data points ofeach sensor may be shifted as compared to the data points of the othersensors. Put another way, while the initial impedance and sensitivityvalues for each sensor may be different, the change in sensitivity andimpedance for each sensor changes at about the same linear rate.

FIG. 28 is a graph further illustrating the linear relationship ofimpedance and sensitivity of Sensors A-G. The graph of FIG. 28 is basedon the same data used in FIG. 27, but the data is graphed in terms ofpercent change rather than in absolute values. As can be seen in FIG.28, Sensors A-G appear to exhibit a very similar correspondence betweenthe change in impedance and change in sensitivity.

A computer-implemented estimative algorithm function can be used tomodel a relationship between the change in sensitivity and change inimpedance values of the sensor data generated by Sensors A-G. Theestimative algorithm function may be formed by applying curve fittingtechniques that regressively fit a curve to data points by adjusting thefunction (e.g., by adjusting constants of the function) until an optimalfit to the available data points is obtained, as discussed above withrespect to forming an estimative curve for a sensitivity profile.Additionally or alternatively, the relationship can be modeled into alook-up table stored in computer memory.

Further to FIG. 28, an estimative curve 2802 of the combined sensor dataof Sensors A-G is also plotted. The curve in FIG. 28 is a straight line,but can be other types of curves depending upon the relationship betweenimpedance and sensitivity of the sensor. As discussed herein, the curve2802 can be used to compensate for sensitivity drift of a sensor.

FIG. 29 and FIG. 30 illustrate compensating sensor data taken by thesame sensors used to derive the estimative curve 2802. The measurementsof FIGS. 29 and 30 were taken at 37C. (Note that estimative curve 2802was derived based on sensor measurements taken at 37 C, as discussedabove with reference to FIGS. 27 and 28.) FIG. 29 is a plot ofuncompensated measurements using Sensors A-G. FIG. 30 is a plot ofpercent estimated sensitivity error of measurements compensated usingthe impedance to sensitivity relationship based on the estimative curve2802. The Mean Absolute Relative Difference (MARD) of the uncompensatedsensor data is 21.8%. The MARD of the compensated data is 1.8%, which isa noticeable improvement over the uncompensated data.

FIGS. 31 and 32 are also plots of uncompensated data and percentsensitivity error of compensated data, respectively. The data in FIGS.31 and 32 are based on measurements using Sensors H-L. Sensors H, J, Kand L were selected from the same lot of sensors as Sensors C, F and Gdescribed with reference to FIG. 27 and Sensor I was selected from thesame lot as Sensors A and D described with reference to FIG. 27.Further, the measurements plotted in FIGS. 31 and 32 were taken at 25 Cinstead of 37 C. The estimative curve 2802 derived from Sensors A-G,however, was used to compensate the data measured by Sensors H-L. (Notethe estimative curve 2802 was also based on measurements taken at 37 C.)The MARD of the uncompensated data is 21.9%, nearly the same as the MARDof 21.8% calculated with respect to the sensor data in FIG. 29. The MARDof the compensated data of FIG. 18 is 4.4%, which is close to, althoughslightly higher than the MARD of the compensated data of FIG. 30, butstill much smaller than the uncompensated MARD.

FIGS. 33 and 34 are graphs of uncompensated data and percent sensitivityerror of compensated data, respectively. The data in FIGS. 33 and 34 arebased on data obtained using Sensors M-Q. Sensors M, O, P and Q wereselected from the same lot as Sensors C, F and G described withreference to FIG. 27 and Sensor N was selected from the same lot asSensors A and D described with reference to FIG. 27. The measurements ofSensors M-Q were taken at 42C. The estimative curve 2802 derived fromSensors A-G was also used to compensate the data in FIG. 34. Here, theMARD of the uncompensated data is 13.1% and the MARD of the compensateddata is 4.6%.

Accordingly, Example 2 illustrates that a change in sensitivity tochange in impedance relationship determined at a first temperature canbe used to compensate for sensitivity drift at temperatures differentfrom the first temperature.

Example 3 Prospective Calibration of Sensor Data Using ImpedanceMeasurements

Example 5 pertains to prospective calibration. Further, in thisexperiment, calibration of sensor data is based on a change ofsensitivity to change in impedance relationship previously derived fromsensors from a different sensor lot. That is, in Example 3, theestimative curve 2802 is used to compensate data obtained using SensorsR-U, each of which was selected from a fourth sensor lot, the fourthsensor lot was not included in the group of sensors used to derive theestimative curve 2802. Example 5 shows that data can be calibrated usinga change in sensitivity to change in impedance relationship derived fromdifferent sensor types than the type of sensor being calibrated. Thiscan indicate that a sensor factory calibration code need not be used tocompensate for sensitivity drift.

FIGS. 35 and 36 illustrate prospectively calibrating sensor dataobtained from Sensors R-U. FIG. 35 is a plot of the percent sensitivitychange versus 30 minutes of each of the sensors over approximately 1400minutes. FIG. 36 illustrates the percent estimated sensitivity error ofthe compensated data. The MARD of the uncompensated sensor data is 24.8%(the MARD of uncompensated data in the example of FIG. 15 was 21.8%) andthe MARD of the compensated sensor data is 6.6% (the MARD of compensateddata in the example of FIG. 16 was 1.8%).

Prospective calibration of sensors will now be discussed with referenceto FIGS. 37-39. Here, sensitivity and impedance data is collected fromSensors V-X, Z and AA having a membrane formed by a dipping process andSensor Y having a membrane formed by a spray process. Referring to FIG.37, an estimative curve 3702 is calculated based on the sensitivity andimpedance data from all six Sensors V-Z and AA (i.e. using sensor dataobtained from both sensors having dipped and sprayed membrane). FIG. 39is a graph of the percent sensitivity change of versus 60 minutes of theuncompensated data of each of the six sensors. FIG. 39 is a graph of thepercent estimated sensitivity error of the data of all six sensors afterbeing compensated using estimative curve 3702 discussed above withrespect to FIG. 37. The MARD of the uncompensated data is 25.3% and theMARD of the compensated data is 5.2%.

Thus, Example 5 indicates that using a change in sensitivity to changein impedance relationship derived from sensors selected from differentlots than the sensor being calibrated can never-the-less significantlycompensate for sensor sensitivity drift. It should be noted that curve3702 is a straight line. It is contemplated that non-linear fits orcorrelations can be used instead, which may yield better results.

Example 4 Effect of Temperature

FIG. 40 illustrates a relationship of temperature on impedance andsensitivity of a sensor. Points 4002 are sensitivity values of a sensormeasured over a three day time period and points 4004 are impedancevalues of the sensor measured over the same time period. In Example 4,the sensor is a transcutaneous-type of sensor. The temperature wasinitially set and maintained at 37 C, then raised to 45 C, and finallylowered to 25 C, as indicated in FIG. 40.

As illustrated in FIG. 40, both sensitivity and impedance of the sensorappear to have an inversely proportional relationship with changes intemperature.

FIG. 41 is a plot of the sensitivity measurement values versus theimpedance measurement values of FIG. 40. FIG. 41 illustrates pointsmeasured during sensor run-in as diamonds and points measured afterrun-in as squares.

Example 5 Temperature Compensation

FIG. 42 illustrates compensating analyte concentration data measured bythe sensor of Example 4 for effects of temperature after sensor run-in.Here, a relationship between impedance and temperature was used tocompensate the sensor data. In this example, the relationship was basedon an estimative curve derived from the data of FIG. 41.

The relationship between sensor sensitivity and different temperaturescan then be mathematically modeled (e.g., by fitting a mathematicalcurve to data using one of the modeling techniques used herein), and themathematical model can then be used to compensate for temperatureeffects on the sensor sensitivity. That is, a sensitivity of a sensor(which is affected by the sensor's temperature) can be determined basedon a measured impedance of the sensor applied to the mathematical curve.Sensor data can then be converted to estimated glucose values based onthe determined sensor sensitivity.

Further to FIG. 42, the MARD of the uncompensated data was calculated as9.3% and the MARD of the compensated data was calculated as 2.8%.

Example 6 Moisture Ingress Detection

Example 6 involves detection of moisture ingress in components of sensorelectronics used to drive a sensor. In this example, an input signal isapplied to the sensor having a frequency ranging from 100 Hz to 1 kHz. Aresponse of the signal is then measured over the range of frequenciesand both impedance and phase change derived therefrom. Next, contactstypically positioned inside a sealed section of a sensor electronicsmodule are wetted. The frequency of an input signal is varied between100 and 1000 Hz. A response is once again measured and both impedanceand phase change is derived.

In a first experiment, the contacts were wetted to the point of causinga gross failure of the sensor. The current of the sensor increased from2.3 nA when dry to 36 nA when wetted. In a second experiment, thecontacts were only slightly wetted, where the current of the sensorincreased from 2.3 nA when dry to 6 nA when wetted.

FIG. 43 is a graph of the impedance and phase changes of the firstexperiment, where the contacts were wetted to a point of causing a grossfailure of the sensor system. Curves 4302 and 4304 are impedance andphase values, respectively, of the sensor prior to the wetting of thecontacts. Curves 4306 and 4308 are impedance and phase values,respectively, of the sensor after wetting the contacts. As illustratedin FIG. 43, the impedance of the dry curve 4302 and the wet curve 4306are noticeably different at around 1000 Hz and the phase of the drycurve 4304 and wet curve 4308 are noticeably different at about 100 Hz.

FIG. 44 is a graph of the impedance and phase changes of the secondexperiment, where the contacts are only slightly wetted. Curves 4402 and4404 are impedance and phase, respectively, of the sensor prior to thewetting of the contacts. Curves 4406 and 4408 are impedance and phase,respectively of the sensor after wetting the contacts. As illustrated inFIG. 44, the impedance of the dry curve 4402 and the wet curve 44006 arenoticeably different at around 100 Hz and the phase of the dry curve4404 and wet curve 4408 are noticeably different around 1 kHz.

Example 7 Membrane Damage Detection

Example 7 involves detection of sensor membrane damage using impedancemeasurements. In this example, the impedance of an analyte sensor ismeasured over a range of frequencies ranging from 100 to 1 kHz. Aportion of the sensor's membrane is then cut using a razor blade tocause membrane damage. The impedance of the sensor is once againmeasured over the same range of frequencies. The impedance measurementsof the sensor before the membrane is cut are then compared to themeasurements of the sensor after the membrane is cut to determinewhether the impedance can be used to detect membrane damage.

FIG. 45 is a graph of the sensor measurements before and after themembrane damage. Curves 4502 and 4504 are impedance and phase,respectively, of the sensor prior to membrane damage and curves 4506 and4508 are impedance and phase, respectively, of the sensor after membranedamage. Both the impedance and phase relationships appear to changearound the 1 kHz frequency. This relationship can then be used to detectmembrane damage.

Example 8 Study Using FFT to Calculate Impedance

FIG. 46 is graph plotting impedance calculated using Fast FourierTransform (FFT) and impedance measured using the Gamry for comparison.FIG. 46 illustrates FFT and corresponding Gamry measurements for 30min., 1.5 hrs., 3 hrs. and 13 hrs. of input data. The FFT datasubstantially tracked the Gamry data until about the 1 kHz frequency.Note, the discrepancy after the 1 kHz frequency in FIG. 33 is believedto be due to known limitations on the system used to calculate the FFT.Thus, it is believed that using FFT can provide accurate impedance dataeven past the 1 kHz spectrum, thereby providing good correspondence toimpedance measured using the Gamry.

Example 9 In Vivo Sensitivity Change Compensation

FIGS. 47-49 relate to compensating for sensitivity changes in a humanusing an in vivo glucose sensor.

FIG. 47 is a graph illustrating in vivo data of sensor impedance andsensitivity of a subcutaneous continuous glucose sensor (also referredto herein as a CGM sensor) over about a 25 hour time period. FIG. 47 issimilar to FIG. 25, but uses in vivo data instead of in vitro data.Sensitivity data was generated using data obtained from a blood glucosefinger stick monitor and raw sensor current measurements using the CGMsensor (i.e. dividing raw CGM sensor current in units of counts bytime-corresponding meter glucose value in units of mg/dL to generate thesensitivity value). The impedance data was generated by applying a stepvoltage to the CGM sensor and calculating an impedance based on the peakcurrent of the response, as discussed herein with reference to FIG. 13.As illustrated in FIG. 47, the impedance and sensitivity data appear togenerally track a similar profile as the in vitro data of FIG. 25.

FIG. 48 is a graph of estimated glucose values using the data generatedby the CGM sensor before and after sensitivity compensation using theimpedance measurements of FIG. 47. The compensated and uncompensateddata was calibrated once using a blood glucose reference metermeasurement at the one hour mark. The compensation was performed in amanner like that described above with respect to the in vitro study ofFIG. 30. FIG. 48 also illustrates blood glucose reference measurementsof the host. Based on the data of FIG. 48, it appears that thecompensated CGM sensor data more closely corresponds to the referencemeasurement data than the uncompensated CGM sensor data.

FIG. 49 is an accuracy plot showing differences between theuncompensated and compensated CGM sensor data and the finger-stick meterreference data, where the reference data corresponds to the zero line ofthe plot. The MARD of the uncompensated CGM sensor data is 28.5% and theMARD of the compensated CGM sensor data is 7.8%. Thus, the compensationappears to improve accuracy of the CGM sensor data.

Example 9 Comparison of a Linear Impendence-to-Sensitivity Correlationto a Non-Linear Impendence-to-Sensitivity Correlation

Laboratory experiments showed that there is an inverse relationshipbetween the changes in sensor sensitivity (drift) and the changes inimpedance. Based on the measured impedance, the sensor sensitivity driftcould be compensated using either linear or non-linear sensorsensitivity-impedance correlation.

The linear correlation can be expressed as the equation ΔS=a*ΔI+b, wherea and b can be pre-determined coefficients determined from prior testingof similar sensors. The non-linear correlation can be expressed asΔS=(a*log(t)+b)*ΔI, where a and b are pre-determined constantsdetermined from prior testing of similar sensors. The prior testing ofsimilar sensors was performed using in vitro bench testing and theconstants were derived using conventional computer plotting techniquesin this experiment.

The linear and non-linear equations are derived from the relationshipsof change in sensitivity and change of impedance at time t since thelast calibration. The percent the change of sensitivity at time t isexpressed as:

ΔS=%change in sensitivity=(Sensitivity at t−Sc)/Sc*100%

where Sc is a sensitivity determined at calibration, and t is the timesince the last calibration.

The sensor impendence change from time t is expressed as:

ΔI=% impedance change=(impedance at t−Ic)/Ic*100%

where Ic is an impedance at the time of calibration.

To calculate the linear correlation, conventional computer plottingsoftware was used to plot change in sensitivity (y-axis) versus changein impendence (x-axis) based on in vitro test data and determine thelinear best fit of the data. In this experiment, the linear best fit wasΔS=−4.807*ΔI−0.742. The linear best fit can is used as the linearsensitivity-to-impedance correlation.

To calculate the non-linear correlation, conventional computer plottingsoftware was used to plot change in sensitivity over change in impedanceΔS/ΔI (y-axis) versus log(t). The linear best fit of the plotted data isthen determined using the plotting software to yield ΔS/ΔI=a*log(t)+b,from which the change is sensitivity is derived: ΔS=(a*log(t)+b)*ΔI. Inthis experiment, the plotting software yieldedΔS/ΔI=−1.175*log(t)−1.233. From this equation, ΔS is derived:ΔS=(−1.175*log(t)−1.233)*ΔI.

The linear or non-linear correlation can then be applied to correct forchanges in sensitivity of the sensor using impedance measurements of thesensor.

FIG. 50 illustrates process 5000 for correcting estimated glucose valuesused in this experiment. Process 5000 begins at block 5002 with sensorinsertion at time t=0. Process 5000 then divides into two branches.

The first branch beings at block 5004, where sensor data is generatedusing the continuous glucose sensor in the form of current or counts.The first branch then proceeds to decision block 5006, where process5000 determines whether a calibration is needed. In this experiment, acalibration is determined to be needed one hour and 24 hours aftersensor insertion. If it is determined that calibration is not needed,then the first branch essentially ends. On the other hand, if it isdetermined that calibration is required, then process 5000 acquires areference measurement using a finger stick meter at blocks 5008 and5010. Sensor data corresponding in time to the reference measurement (attime Tc) is determined at block 5012 and used to calculate a sensorsensitivity, Sc, along with the reference data at block 5014.

The second branch of process 5000 begins with measuring an impendence ofthe sensor at block 5016. The impedance is measured using the peakcurrent technique discussed in this application with reference to FIG.13. Next it is determined if calibration is needed in decision block5018. In this experiment, this is the same decision made in decisionblock 5006. If calibration is determined to be needed, then theimpedance measured in block 5016 is flagged and stored as impedance Ic(impedance calibration) at block 5020. If calibration is not needed,then process 5000 skips block 5020 and proceeds to determine a change inimpedance at block 5022 from the impedance flagged as impedance Ic,which in this experiment is the difference between the impendencemeasured in block 5016 and the impedance Ic. A compensation algorithm(using the linear or the non-linear compensation algorithm) is then usedin block 5024 to calculate a change in sensitivity at block 5026 basedon the change in impedance determined at block 5022.

The first and second branches of process 5000 then merge at block 5028where a corrected sensitivity is determined. The corrected sensitivityis then used to convert sensor data in units of current or counts to anestimated glucose value in units of glucose concentration.

Process 5000 is repeated for each sensor data point that is convertedinto an estimated glucose value.

In this experiment, sensor performance improvement by impedancecompensation is demonstrated below. The sensors were tested usingcontinuous glucose sensors placed in sheep serum for 2 days. The sensorsensitivity was calculated at 1 hour and 24 hours. In FIG. 51, thesensor sensitivity obtained at calibrations (1 hour or 24 hours) wasused throughout the day without any correction. In FIG. 52, sensitivitywas corrected using linear impedance correction in accordance with theprocess illustrated in FIG. 50. In FIG. 53, sensitivity was adjustedwith non-linear sensitivity-impedance correlation in accordance with theprocess illustrated in FIG. 50. While both linear and non-linearcorrections demonstrated improved sensor performance, the non-linearcorrection produced better results.

Some embodiments disclosed herein continuously or iteratively apply astimulus signal during a continuous sensor session or use andextrapolate information from the output associated with the stimulus,such as using a peak current measurement, EIS, etc. Although certainelectrochemical analysis techniques were described herein, it isunderstood that many other techniques can be used instead to detectcharacteristics of sensors described herein, such as voltammetry,chronopotentiometry, current or potential step techniques, differentialchrono-potentiometry, oxygen absorption rate measurements,potential/current sweep or pulse methods, etc.

While the disclosure has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Thedisclosure is not limited to the disclosed embodiments. Variations tothe disclosed embodiments can be understood and effected by thoseskilled in the art in practicing the claimed disclosure, from a study ofthe drawings, the disclosure and the appended claims.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

1. A method for calibrating an analyte sensor, the method comprising:applying a time-varying signal to the analyte sensor; measuring a signalresponse to the applied signal; determining, using sensor electronics, asensitivity of the analyte sensor, the determining comprisingcorrelating at least one property of the signal response to apredetermined sensor sensitivity profile; and generating, using sensorelectronics, estimated analyte concentration values using the determinedsensitivity and sensor data generated by the analyte sensor.
 2. Themethod of claim 1, wherein the sensitivity profile comprises varyingsensitivity values over time since implantation of the sensor.
 3. Themethod of claim 1, wherein the predetermined sensitivity profilecomprises a plurality of sensitivity values.
 4. The method of claim 1,wherein the predetermined sensitivity profile is based on sensorsensitivity data generated from studying sensitivity changes of analytesensors similar to the analyte sensor.
 5. The method of claim 1, furthercomprising applying a bias voltage to the sensor, wherein thetime-varying signal comprises a step voltage above the bias voltage or asine wave overlaying a voltage bias voltage.
 6. The method of claim 1,wherein the determining further comprises calculating an impedance valuebased on the measured signal response and correlating the impedancevalue to a sensitivity value of the predetermined sensitivity profile.7. The method of claim 1, further comprising applying a DC bias voltageto the sensor to generate sensor data, wherein the estimating analyteconcentration values includes generating corrected sensor data using thedetermined sensitivity.
 8. The method of claim 7, further comprisingapplying a conversion function to the corrected sensor data to generatethe estimated analyte concentration values.
 9. The method of claim 7,further comprising forming a conversion function based at least in partof the determined sensitivity, and wherein the conversion function isapplied to the sensor data to generate the estimated analyteconcentration values.
 10. The method of claim 1, wherein the property isa peak current value of the signal response.
 11. The method of claim 1,wherein the determining further comprises using at least one ofperforming a Fast Fourier Transform on the signal response data,integrating at least a portion of a curve of the signal response, anddetermining a peak current of the signal response.
 12. The method ofclaim 11, further comprising generating estimated analyte concentrationvalues using the selected sensitivity profile.
 13. The method of claim1, wherein the determining further comprises selecting the predeterminedsensitivity profile based on the determined sensor property from aplurality of different predetermined sensitivity profiles.
 14. Themethod of claim 13, wherein the selecting includes performing a dataassociation analysis to determine a correlation between the determinedsensor property and each of the plurality of different predeterminedsensitivity profiles and wherein the selected predetermined sensitivityprofile has the highest correlation.
 15. The method of claim 13, furthercomprising generating estimated analyte concentration values using theselected sensitivity profile.
 16. The method of claim 15, furthercomprising determining a second sensitivity value using the selectedsensitivity profile, wherein a first set of estimated analyteconcentration values is generated using the determined sensitivity valueand sensor data associated with a first time period, and wherein asecond set of concentration values is generated using the secondsensitivity value and sensor data associated with a second time period.17. A system for measuring an analyte, the system comprising sensorelectronics configured to be operably connected to a continuous analytesensor, the sensor electronics configured to: apply a time-varyingsignal to the analyte sensor; measure a signal response to the appliedsignal; determine a sensitivity of the analyte sensor, the determiningcomprising correlating at least one property of the signal response to apredetermined sensor sensitivity profile; and generate estimated analyteconcentration values using the determined sensitivity and sensor datagenerated by the analyte sensor.
 18. The system of claim 17, wherein thesensitivity profile comprises varying sensitivity values over time sinceimplantation of the sensor.
 19. The system of claim 17, wherein thepredetermined sensitivity profile comprises a plurality of sensitivityvalues.
 20. The system of claim 17, wherein the predeterminedsensitivity profile is based on sensor sensitivity data generated fromstudying sensitivity changes of analyte sensors similar to the analytesensor.
 21. The system of claim 17, wherein the sensor electronics arefurther configured to apply a bias voltage to the sensor, wherein thetime-varying signal comprises a step voltage above the bias voltage or asine wave overlaying a voltage bias voltage.
 22. The system of claim 17,wherein the determining further comprises calculating an impedance valuebased on the measured signal response and correlating the impedancevalue to a sensitivity value of the predetermined sensitivity profile.23. The system of claim 17, wherein the sensor electronics are furtherconfigured to apply a DC bias voltage to the sensor to generate sensordata, wherein the estimating analyte concentration values includesgenerating corrected sensor data using the determined sensitivity. 24.The system of claim 17, wherein the sensor electronics are furtherconfigured to apply a conversion function to the corrected sensor datato generate the estimated analyte concentration values.
 25. The systemof claim 17, wherein the sensor electronics are further configured toform a conversion function based at least in part of the determinedsensitivity, and wherein the conversion function is applied to thesensor data to generate the estimated analyte concentration values. 26.The system of claim 17, wherein the property is a peak current value ofthe signal response.
 27. The system of claim 17, wherein the determiningfurther comprises using at least one of performing a Fast FourierTransform on the signal response data, integrating at least a portion ofa curve of the signal response, and determining a peak current of thesignal response.
 28. The system of claim 27, wherein the sensorelectronics are further configured to generate estimated analyteconcentration values using the selected sensitivity profile.
 29. Thesystem of claim 17, wherein the determining further comprises selectingthe predetermined sensitivity profile based on the determined sensorproperty from a plurality of different predetermined sensitivityprofiles.
 30. The system of claim 29, wherein the selecting includesperforming a data association analysis to determine a correlationbetween the determined sensor property and each of the plurality ofdifferent predetermined sensitivity profiles and wherein the selectedpredetermined sensitivity profile has the highest correlation.
 31. Thesystem of claim 29, wherein the system electronics are furtherconfigured to generate estimated analyte concentration values using theselected sensitivity profile.
 32. The system of claim 31, wherein thesystem electronics are further configured to determine a secondsensitivity value using the selected sensitivity profile, wherein afirst set of estimated analyte concentration values is generated usingthe determined sensitivity value and sensor data associated with a firsttime period, and wherein a second set of concentration values isgenerated using the second sensitivity value and sensor data associatedwith a second time period.
 33. The system of claim 17, wherein thesensor electronics comprise a processor module, the processor modulecomprising instructions stored in computer memory, wherein theinstructions, when executed by the processor module, cause the sensorelectronics to perform the applying, the measuring, the determining andthe generating.